Clustering is one of the most fundamental and essential data analysis tasks with broad applications. It has been studied in various research fields: data mining, machine learning, pattern recognition and in engineering, economics and biomedical data analysis. Headache is not a disease that typically shortens one’s life, but it can be a serious social as well as a health problem. Approximately 27 billion euros per year are lost through reduced work productivity in the European community. This paper is focused on (...) a new strategy based on a hybrid model for combining fuzzy partition method and maximum likelihood estimation clustering algorithm for diagnosing primary headache disorder. The proposed hybrid system is tested on two data sets for diagnosing headache disorder collected from Clinical Centre of Vojvodina in Serbia. (shrink)

The paper presents different clustering approaches in legal judgments from the Special Civil Court located at the Federal University of Santa Catarina. The subject is Consumer Law, specifically cases in which consumers claim moral and material compensation from airlines for service failures. To identify patterns from the dataset, we apply four types of clustering algorithms: Hierarchical and Lingo, K-means and Affinity Propagation. We evaluate the results based on the following criteria: entropy and purity; algorithm's ability in providing labels; legal expert’s (...) evaluation; and experimental complexity. The results demonstrate that the most advantageous approach is Hierarchical Clustering, since it has the best entropy and purity numbers, as well as the least difficulty for the expert to analyze the clusters, and the least experimental complexity. The main contribution of the paper is to show the advantages and disadvantages of each approach, especially to identify labels in unstructured and non-indexed legal texts. (shrink)

Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user’s perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) (...) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution. (shrink)

Statistical power is often a concern for clustered randomized control trials (RCTs) due to variance inflation from design effects and the high cost of adding study clusters (such as hospitals, schools, or communities). While covariate pre-specification can improve power for estimating regression-adjusted average treatment effects (ATEs), further precision gains can be achieved through covariate selection once primary outcomes have been collected. This article uses design-based methods underlying clustered RCTs to develop Lasso methods for the post-hoc selection of covariates for ATE (...) estimation that avoids a lack of transparency and model overfitting. Our focus is on two-stage estimators: in the first stage, Lasso estimation is conducted using data on cluster-level averages or sums, and in the second stage, standard ATE estimators are adjusted for covariates using the first-stage Lasso results. We discuss l 1 {l}_{1} consistency of the estimated Lasso coefficients, asymptotic normality of the ATE estimators, and design-based variance estimation. The nonparametric approach applies to continuous, binary, and discrete outcomes. We present simulation results and demonstrate the method using data from a federally funded clustered RCT testing the effects of school-based programs promoting behavioral health. (shrink)

Volume 19, Issue 10, October 2019, Page 101-103.

erative clustering. First, we show formally that the common heuristic agglomerative clustering algorithms – Ward’s method, single-link, complete-link, and a variant of group-average – are each equivalent to a hierarchical model-based method. This interpretation gives a theoretical explanation of the empirical behavior of these algorithms, as well as a principled approach to resolving practical issues, such as number of clusters or the choice of method. Second, we show how a model-based viewpoint can suggest variations on these basic agglomerative algorithms. (...) We introduce adjusted complete-link, Mahalanobis-link, and line-link as variants, and demonstrate their utility. (shrink)

This thesis addresses the tension in pragmatic cluster-randomized trials between their social value and the requirement to respect the autonomy of research participants. Pragmatic trials are designed to evaluate the effectiveness of treatments in real-world settings to inform clinical decision-making and promote cost-efficient care. These trials are often embedded into clinical settings and ideally include all patients who would receive the treatments under investigation as a part of routine care. Trialists increasingly adopt cluster-randomized designs—in which intact groups, such (...) as hospitals or clinics, are allocated randomly to study interventions—to simplify the inclusion of all patients. But including all-comers conflicts with the requirement to obtain written informed consent from research participants. Since informed consent is central to respecting patient autonomy, the question arises: how can the ends of autonomy and pragmatism be served simultaneously? Some philosophers argue that patients have an obligation to participate in clinical research and that this may negate consent requirements. I argue that while there may be grounds for a prima facie obligation for patients to participate in clinical research, no compelling argument has demonstrated that the obligation is enforceable. Others assert that broad application of a waiver of consent will facilitate the conduct of pragmatic cluster-randomized trials. I demonstrate that this proposal sharply conflicts with the historical origins of the waiver. I articulate a novel moral foundation for the use of a waiver of consent and show that when trials evaluate treatments delivered directly to patients, the autonomy interests at stake for participants are too substantial to permit its use. My solution draws a distinction between consent requirements in existing policy and consent as an autonomous authorization. As many pragmatic cluster-randomized trials are conducted in primary care settings with no research staff, I argue that patient autonomy can be promoted and protected using clinical-style consent, in which health providers seek verbal informed consent from patients and document it in the electronic health record. This approach has been associated with high rates of recruitment and, thus, may satisfy both requirements for social value and respect for autonomy. (shrink)

Our prominent definitions of cognition are too vague and lack empirical grounding. They have not kept up with recent developments, and cannot bear the weight placed on them across many different debates. I here articulate and defend a more adequate theory. On this theory, behaviors under the control of cognition tend to display a cluster of characteristic properties, a cluster which tends to be absent from behaviors produced by non-cognitive processes. This cluster is reverse-engineered from the empirical (...) tests that comparative psychologists use to determine whether a behavior was generated by a cognitive or a non-cognitive process. Cognition should be understood as the natural kind of psychological process that non-accidentally exhibits the properties assessed by these tests (as well as others we have not yet discovered). Finally, I review two plausible neural accounts of cognition's underlying mechanisms?one based in localization of function to particular brain regions and another based in the more recent distributed networks approach to neuroscience?which would explain why these properties non-accidentally cluster. While this notion of cognition may be useful for a number of debates, I here focus on its application to a recent crisis over the distinction between cognition and association in comparative psychology. (shrink)

The cluster account of art is a purportedly non-definitional account of art, inspired by Wittgenstein's notion of family resemblance, and recently defended by Berys Gaut. Gaut does not provide good reasons to think that art is not definable, and his approach to possible counterexamples to the cluster account would, applied consistently, preclude this. The cluster account's theory of error, its resources for accounting for borderline cases, and its heuristic usefulness are not impressive. Reasons strong enough to (...) warrant accepting the cluster account, it is concluded, have not been given. (shrink)

IntroductionSymptoms of schizophrenia are closely related to aberrant language comprehension and production. Macroscopic brain changes seen in some patients with schizophrenia are suspected to relate to impaired language production, but this is yet to be reliably characterized. Since heterogeneity in language dysfunctions, as well as brain structure, is suspected in schizophrenia, we aimed to first seek patient subgroups with different neurobiological signatures and then quantify linguistic indices that capture the symptoms of “negative formal thought disorder”.MethodsAtlas-based cortical thickness values of 66 (...) patients with first-episode psychosis and 36 healthy controls were analyzed with hierarchical clustering algorithms to produce neuroanatomical subtypes. We then examined the generated subtypes and investigated the quantitative differences in MRS-based glutamate levels [in the dorsal anterior cingulate cortex ] as well as in three aspects of language production features: fluency, syntactic complexity, and lexical cohesion.ResultsTwo neuroanatomical subtypes among patients were observed, one with near-normal cortical thickness patterns while the other with widespread cortical thinning. Compared to the subgroup of patients with relatively normal cortical thickness patterns, the subgroup with widespread cortical thinning was older, with higher glutamate concentration in dACC and produced speech with reduced mean length of T-units and lower repeats of content words, despite being equally fluent.ConclusionWe characterized a patient subgroup with thinner cortex in first-episode psychosis. This subgroup, identifiable through macroscopic changes, is also distinguishable in terms of neurochemistry and language behavior. This study supports the hypothesis that glutamate-mediated cortical thinning may contribute to a phenotype that is detectable using the tools of computational linguistics in schizophrenia. (shrink)

Two studies explored individual differences in appraisal of minor, potentially stressful events. Previous research on appraisal has focused on one or two appraisal dimensions within specific situations rather than on the full range of appraisals or on the stability of appraisal across situations. Goals of the present studies were: (1) to explore stability of individual differences in appraisal across situations; (2) to identify individual differences in general appraisal styles; and (3) to examine how appraisal styles are related to personality constructs. (...) Appraisal was moderately stable across situations and individual differences in appraisal style were related to relevant personality constructs. Primary threat appraisals and secondary appraisals of blame and manageability emerged as particularly important in distinguishing the appraisal styles. Implications of the results for the development of psychopathology are considered. (shrink)

Our prominent definitions of cognition are too vague and lack empirical grounding. They have not kept up with recent developments, and cannot bear the weight placed on them across many different debates. I here articulate and defend a more adequate theory. On this theory, behaviors under the control of cognition tend to display a cluster of characteristic properties, a cluster which tends to be absent from behaviors produced by non-cognitive processes. This cluster is reverse-engineered from the empirical (...) tests that comparative psychologists use to determine whether a behavior was generated by a cognitive or a non-cognitive process. Cognition should be understood as the natural kind of psychological process that non-accidentally exhibits the properties assessed by these tests (as well as others we have not yet discovered). Finally, I review two plausible neural accounts of cognition's underlying mechanisms—one based in localization of function to particular brain regions and another based in the more recent distributed networks approach to neuroscience—which would explain why these properties non-accidentally cluster. While this notion of cognition may be useful for a number of debates, I here focus on its application to a recent crisis over the distinction between cognition and association in comparative psychology. (shrink)

The nature of the process of meaning-making in science has been one of the central problems in the philosophy of science of the 20th century. Yet, in spite of strenuous efforts by many able philosophers and historians of science over the past three decades or so, our understanding of this process continues to be unsatisfactory and fragmented at best. The need for an adequate account has been particularly exacerbated by the "infamous" and often misinterpreted problem of incommensurability , and its (...) alleged consequence, the incomparability of scientific theories--which presumably threatens the rationality, objectivity, and progress of science. ;In this project, I argue that a new and revised cluster theory can be articulated, which meets the objections typically raised against traditional theories of meaning and theories of reference . Such a theory is not only based on more plausible assumptions and principles, but, in addition, it satisfies the main adequacy requirements formulated by proponents of a "cognitive-historical approach" . I am thus concerned not just with refuting "the entering wedge" of the argument against a defense of cluster theory, but with offering a relatively developed theory, sufficiently fleshed out to permit appreciation of its distinctiveness and evaluation of its merits. ;I argue that the new cluster theory provides not only an adequate account of the process of meaning-making in science, but also a nuanced and context-sensitive one, which exhibits the fine-structure of the history of science. It is thus capable of accounting for the different kinds and degrees of meaning and reference changes in science. Furthermore, when applied in a case-study of the "chemical revolution," it accounts for that which has escaped change without discontinuity, or even, as result of a broader framework of continuous conceptual change. The new cluster theory constitutes a proposal showing how the comparability of scientific theories is possible, how we have in fact been comparing them all along, despite "local incommensurabilities" of various kinds and degrees. Such a theory offers new insights into the developments of the chemical revolution in particular, but also into the structure and process of scientific revolutions in general. In short, it gives us a new framework for understanding the rationality, objectivity, and progress of science. (shrink)

In this paper, we present our research on data mining approaches with the existence of obstacles. Although there are a lot of algorithms designed to detect clusters with obstacles, few algorithms can detect clusters and outliers simultaneously and interactively. We here extend our original research [Shi, Zhang, Towards Exploring Interactive Relationship between Clusters and Outliers in Multi-Dimensional Data Analysis, 518–519: IEEE Computer Society, 2005] on iterative cluster and outlier detection to study the problem of detecting cluster and outliers (...) iteratively with the presence of obstacles. Clusters and outliers are concepts of the same importance, so it is necessary to treat clusters and outliers in the same way in data analysis. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing. (shrink)

Correct interpretation of the data from integral laboratory tests, including Thrombin Generation Test, requires biochemistry-based mathematical models of blood coagulation. The purpose of this study is to describe the experimental TGT data from healthy donors and hemophilia A and B patients. We derive a simplified ODE model and apply it to analyze the TGT data from healthy donors and HA/HB patients with in vitro added tissue factor pathway inhibitor antibody. This model allows the characterization of hemophilia patients in the space (...) of three most important model parameters. The proposed approach may provide a new quantitative tool for the analysis of experimental TGT. Also, it gives a reduced model of coagulation verified against clinical data to be used in future theoretical large-scale modeling of thrombosis in flow. (shrink)

Aesthetic emotions are elicited by different sensory impressions generated by music, visual arts, literature, theater, film, or nature scenes. Recently, the AESTHEMOS scale has been developed to facilitate the empirical assessment of such emotions. In this article we report a semantic profile analysis of aesthetic emotion terms that had been used for the development of this scale, using the GRID approach. This method consists of obtaining ratings of emotion terms on a set of meaning facets which represent five components (...) of the emotion process. The aims here were to determine the dimensionality of the GRID features when applied to aesthetic emotions and compare it to published results for emotion terms in general, and to examine the internal organization of the domain of aesthetic emotion terms in order to identify salient clusters of these items based on the similarity of their feature profiles on the GRID. Exploratory Principal Component Analyses suggest a four-dimensional structure of the semantic space consisting of valence, power, arousal, and novelty, converging with earlier GRID studies on large sets of standard emotion terms. Using cluster analyses, 15 clusters of aesthetic emotion terms with similar GRID feature profiles were identified, revealing the internal organization of the aesthetic emotion terms domain and meaningful subgroups of aesthetic emotions. While replication for further languages is required, these findings provide a solid basis for further research and methodological development in the realm of aesthetic emotions. (shrink)

This article argues why and how a participatory approach to implement corporate social responsibility (CSR) in a cluster would be beneficial for small-and medium-sized enterprises (SMEs) who are members of the NCE -Subsea cluster in Bergen, Norway. The political and strategic reasons as well as internal motivation for SMEs to incorporate CSR into their business strategies are discussed with support from relevant literature. Furthermore, we offer a discussion on the characteristics of different approaches to incorporating CSR as (...) part of business strategy and provide examples from the Norwegian context. As part of this discussion, we look into some of the clusters in Europe which have adopted a systematic approach to implement CSR as part of their agenda. We propose a means to undertake CSR as part of the cluster through a network model to address the limitations faced by the SMEs when they try to implement CSR individually. A network model (cluster approach) to CSR will motivate the uptake of CSR among SMEs when the network (cluster) is characterized by close geographical proximity and operates in the same sector. The uptake of CSR as part of the network (cluster) agenda can also lead to innovation through cooperation and competition. The particular challenges faced by the SMEs in implementing and sustaining CSR can be also minimized by being part of a network (cluster). (shrink)

Data mining’s primary clustering method has several uses, including gene analysis. A set of unlabeled data is divided into clusters using data features in a clustering study, which is an unsupervised learning problem. Data in a cluster are more comparable to one another than to those in other groups. However, the number of clusters has a direct impact on how well the K-means algorithm performs. In order to find the best solutions for these real-world optimization issues, it is necessary (...) to use techniques that properly explore the search spaces. In this research, an enhancement of K-means clustering is proposed by applying an equilibrium optimization approach. The suggested approach adjusts the number of clusters while simultaneously choosing the best attributes to find the optimal answer. The findings establish the usefulness of the suggested method in comparison to existing algorithms in terms of intra-cluster distances and Rand index based on five datasets. Through the results shown and a comparison of the proposed method with the rest of the traditional methods, it was found that the proposal is better in terms of the internal dimension of the elements within the same cluster, as well as the Rand index. In conclusion, the suggested technique can be successfully employed for data clustering and can offer significant support. (shrink)

This article argues why and how a participatory approach to implement corporate social responsibility (CSR) in a cluster would be beneficial for small- and medium-sized enterprises (SMEs) who are members of the NCE – Subsea cluster in Bergen, Norway. The political and strategic reasons as well as internal motivation for SMEs to incorporate CSR into their business strategies are discussed with support from relevant literature. Furthermore, we offer a discussion on the characteristics of different approaches to incorporating (...) CSR as part of business strategy and provide examples from the Norwegian context. As part of this discussion, we look into some of the clusters in Europe which have adopted a systematic approach to implement CSR as part of their agenda. We propose a means to undertake CSR as part of the cluster through a network model to address the limitations faced by the SMEs when they try to implement CSR individually. A network model (cluster approach) to CSR will motivate the uptake of CSR among SMEs when the network (cluster) is characterized by close geographical proximity and operates in the same sector. The uptake of CSR as part of the network (cluster) agenda can also lead to innovation through cooperation and competition. The particular challenges faced by the SMEs in implementing and sustaining CSR can be also minimized by being part of a network (cluster). (shrink)

Traditional document clustering algorithms consider text-based features such as unique word count, concept count, etc. to cluster documents. Meanwhile, event mining is the extraction of specific events, their related sub-events, and the associated semantic relations from documents. This work discusses an approach to event mining through clustering. The Universal Networking Language -based subgraph, a semantic representation of the document, is used as the input for clustering. Our research focuses on exploring the use of three different feature sets for (...) event clustering and comparing the approaches used for specific event mining. In our previous work, the clustering algorithm used UNL-based event semantics to represent event context for clustering. However, this approach resulted in different events with similar semantics being clustered together. Hence, instead of considering only UNL event semantics, we considered assigning additional weights to similarity between event contexts with event-related attributes such as time, place, and persons. Although we get specific events in a single cluster, sub-events related to the specific events are not necessarily in a single cluster. Therefore, to improve our cluster efficiency, connective terms between two sentences and their representation as UNL subgraphs were also considered for similarity determination. By combining UNL semantics, event-specific arguments similarity, and connective term concepts between sentences, we were able to obtain clusters for specific events and their sub-events. We have used 112 000 Tamil documents from the Forum for Information Retrieval Evaluation data corpus and achieved good results. We have also compared our approach with the previous state-of-the-art approach for Router-RCV1 corpus and achieved 30% improvements in precision. (shrink)

Conceptions of human nature fall under two broad categories, trait bin accounts and trait cluster accounts. Trait bin accounts take there to be a special bin of traits, one composed of all and only those traits constituting our nature. For those arguing for a trait bin account of human nature, the challenge is to articulate what it is that marks a trait as being in or outside of the bin. For some, the bin is filled by the traits essential (...) to being human. Others, such as Machery in his contribution to this volume, offer a non-essentialist trait bin conception of human nature. In this chapter, I argue that trait bin approaches to human nature are misguided, that there is no good way of dividing human traits into those that are a part of our nature and those that are not. Instead, I argue for a trait cluster account, which sees human nature not as a special bin of traits, but as the relationship among traits. Under this account, human nature lies in the patterns of trait expression within and across human life histories. This account does a superior job characterizing what humans are like and aligning human nature with the human sciences. (shrink)

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if (...) in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method’s sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results. (shrink)

Despite the burgeoning literature on professionalism in other health professions, psychology lags behind in the level of attention given to this core competency. In this article, we review definitions from other health professions and how they address professionalism. Next, we review how this competency evolved within health service psychology (HSP), and we propose a definition. We offer an approach for assessing professionalism within HSP. Consideration is given to strategies and methods for providing effective education and training in this multifaceted (...) competency. Finally, recommendations are made for creating a culture of professionalism within HSP and honoring psychology’s social contract with multiple publics. (shrink)

In this paper, a size-independent modification of the general detrended fluctuation analysis method is introduced. With this modified DFA, seismic time series pertaining to most seismically active regions of the world from the year1972up to the year2016are comparatively analyzed. An eminent homogeneity of spatial clustering behaviors in worldwide range is detected and DFA scaling exponents coincide with previous results for local regions. Furthermore, universal nontrivial spatial clustering behaviors are revealed from shallow to intermediate-depth earthquakes by varying the depth of the (...) hypocenter: in shallow earthquakes, the depth range corresponding to the minimum spatial clustering is first verified to be spatiotemporal and identical; in intermediate-depth earthquakes, strong weakening long-range correlations compared to shallow earthquakes are unveiled to strengthen the recent findings in seismology. Our work aims to suggest a possible statistical approach to explore the dynamic mechanisms implied in the seismicity as well as in other analogous dynamic evolution processes. (shrink)

Our beliefs and opinions are shaped by others, making our social networks crucial in determining what we believe to be true. Sometimes this is for the good because our peers help us form a more accurate opinion. Sometimes it is for the worse because we are led astray. In this context, we address via agent-based computer simulations the extent to which patterns of connectivity within our social networks affect the likelihood that initially undecided agents in a network converge on a (...) true opinion following group deliberation. The model incorporates a fine-grained and realistic representation of belief and trust, and it allows agents to consult outside information sources. We study a wide range of network structures and provide a detailed statistical analysis concerning the exact contribution of various network metrics to collective competence. Our results highlight and explain the collective risks involved in an overly networked or partitioned society. Specifically, we find that 96% of the variation in collective competence across networks can be attributed to differences in amount of connectivity and clustering, which are negatively correlated with collective competence. A study of bandwagon or “group think” effects indicates that both connectivity and clustering increase the probability that the network, wholly or partly, locks into a false opinion. Our work is interestingly related to Gerhard Schurz’s work on meta-induction and can be seen as broadly addressing a practical limitation of his approach. (shrink)

Mining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to (...) define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns. (shrink)

Mental health professionals working in hospitals or community clinics inevitably face the realisation that we possess imperfect conceptual means to understand mental disorders. In this paper the authors bring together ideas from the fields of Philosophy, Psychiatry, Cognitive Psychology and Linguistics to reflect on the ways we represent phenomena of high practical importance that we often take for granted, but are nevertheless difficult to define in ontological terms. The paper follows through the development of the concept of psychosis over the (...) last two centuries in the interplay of three different conceptual orientations: the categorical, dimensional and network approaches. Each of these represent the available knowledge and dominant thinking styles of the era in which they emerged and take markedly different stances regarding the nature of mental phenomena. Without particular commitment to any ontological positions or models described, the authors invite the reader into a thinking process about the strengths and weaknesses of these models, and how they can be reconciled in multidisciplinary settings to benefit the process of patient care. (shrink)

No other cluster of medical issues affects the genders as differently as those related to procreationcontraception, sterilization, abortion, artificial insemination, in-vitro fertilization, surrogate motherhood, and genetic screening. Rosemarie Tong s approach to feminist bioethics serves as a catalyst to bring together different feminist voices in hope of actually doing something to make gender equity a present reality rather than a mere future possibility.".

Clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets are a useful means to describe and handle indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-value neutrosophic information, the article proposes a single-valued neutrosophic minimum spanning tree clustering algorithm. Firstly, we defined a generalized distance measure between SVNSs. Then, we present an SVNMST clustering algorithm for clustering single-value (...) neutrosophic data based on the generalized distance measure of SVNSs. Finally, two illustrative examples are given to demonstrate the application and effectiveness of the developed approach. (shrink)

The cultural evolutionary approach to the dynamics of cumulative culture is insufficient for understanding how culture affects heritability estimates; it ignores the agency of individuals and internal complexity of social groups that drive cultural evolution. Both environmental and social selection need consideration. The WEIRD (western, educated, industrialized, rich, and democratic) problem has never plagued anthropology: A wealth of ethnography is available for the problem at hand.

Philosophical treatments of natural kinds are embedded in two distinct projects. I call these the philosophy of science approach and the philosophy of language approach. Each is characterized by its own set of philosophical questions, concerns, and assumptions. The kinds studied in the philosophy of science approach are projectible categories that can ground inductive inferences and scientific explanation. The kinds studied in the philosophy of language approach are the referential objects of a special linguistic category—natural kind (...) terms—thought to refer directly. Philosophers may hope for a unified account addresses both sets of concerns. This paper argues that this cannot be done successfully. No single account can satisfy both the semantic objectives of the philosophy of language approach and the explanatory projects of the philosophy of science approach. After analyzing where the tensions arise, I make recommendations about assumptions and projects that are best abandoned, those that should be retained, and those that should go their separate ways. I also recommend adopting the disambiguating terminology of “scientific kinds” and “natural kinds” for the different notions of kinds developed in these different approaches. (shrink)

Introduction Adolescents’ physical activity (PA) behavior can be driven by several psychosocial determinants at the same time. Most analyses use a variable-based approach that examines relations between PA-related determinants and PA behavior on the between-person level. Using this approach, possible coexistences of different psychosocial determinants within one person cannot be examined. Therefore, by applying a person-oriented approach, this study examined a) which profiles regarding PA-related psychosocial variables typically occur in female sixth-graders, b) if these profiles deliver a (...) self-consistent picture according to theoretical assumptions, and c) if the profiles contribute to the explanation of PA. Materials and Methods The sample comprised 475 female sixth-graders. Seventeen PA-related variables were assessed: support for autonomy, competence and relatedness in PE as well as their satisfaction in PE and leisure-time; behavioral regulation of exercise (five subscales); self-efficacy and social support from friends and family (two subscales). Moderate-to-vigorous PA was measured using accelerometers. Data were analyzed using the self-organizing maps (SOM) analysis, a cluster analysis including an unsupervised algorithm for non-linear models. Results According to the respective level of psychosocial resources, a positive, a medium and a negative cluster were identified. This superordinate cluster solution represented a self-consistent picture that was in line with theoretical assumptions. The three-cluster solution contributed to the explanation of PA behavior, with the positive cluster accumulating an average of six minutes more moderate-to-vigorous PA per day than the medium cluster and ten minutes more than the negative cluster. Additionally, SOM detected a subgroup within the positive cluster that benefited from a specific combination of intrinsic and external regulations with regard to PA. Discussion The results underline the relevance of the assessed psychosocial determinants of PA behavior in female sixth-graders. The results further indicate that the different psychosocial resources within a given person do not develop independently of one another, which supports the use of a person-oriented approach. In addition, the SOM analysis identified subgroups with specific characteristics, which would have remained undetected using variable-based approaches. Thus, this approach offers the possibility to reduce data complexity without overlooking subgroups with special demands that go beyond the superordinate cluster solution. (shrink)

Despite many attempts to achieve an adequate definition of living systems by means of a set of necessary and sufficient conditions, the opinion that such an enterprise is inexorably destined to fail is increasingly gaining support. However, we believe options do not just come down to either having faith in a future success or endorsing skepticism. In this paper, we aim to redirect the discussion of the problem by shifting the focus of attention from strict definitions towards a philosophical framework (...) that allows conceiving of living systems as a natural kind, but whereby natural kinds are not to be defined by fixed necessary and sufficient conditions. We argue for a property-cluster kind approach according to which living systems constitute a natural kind with vague boundaries, capable of changing, and whose members do not need to instantiate every property. We draw from Boyd’s homeostatic property-cluster theory and introduce two modifications, one regarding homeostatic mechanisms and another related to the scientific role of kinds. Thus, our view overcomes some difficulties of Boyd’s theory and we are able to account for the natural kindhood of living things. We also emphasize the most appealing features of our approach for specific research fields and address three objections to this sort of approach. (shrink)

In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to support legal (...) experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data. (shrink)

In synthetic aperture radar image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the (...) accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex. (shrink)

The appropriate understanding and fast processing of lengthy legal documents are computationally challenging problems. Designing efficient automatic summarization techniques can potentially be the key to deal with such issues. Extractive summarization is one of the most popular approaches for forming summaries out of such lengthy documents, via the process of summary-relevant sentence selection. An efficient application of this approach involves appropriate scoring of sentences, which helps in the identification of more informative and essential sentences from the document. In this (...) work, a novel sentence scoring approach DCESumm is proposed which consists of supervised sentence-level summary relevance prediction, as well as unsupervised clustering-based document-level score enhancement. Experimental results on two legal document summarization datasets, BillSum and Forum of Information Retrieval Evaluation (FIRE), reveal that the proposed approach can achieve significant improvements over the current state-of-the-art approaches. More specifically it achieves ROUGE metric F1-score improvements of (1−6)% and (6−12)% for the BillSum and FIRE test sets respectively. Such impressive summarization results suggest the usefulness of the proposed approach in finding the gist of a lengthy legal document, thereby providing crucial assistance to legal practitioners. (shrink)