Similarity profiled association mining from time stamped transaction databases is an important topic of research relatively less addressed in the field of temporal data mining. Mining temporal patterns from these time series databases requires choosing and applying similarity measure for similarity computations and subsequently pruning temporal patterns. This research proposes a novel z-space based interest measure named as Krishna Sudarsana for time-stamped transaction databases by extending interest measure Srihass proposed in previous research. Krishna Sudarsana is designed by using the product (...) based fuzzy Gaussian membership function and performs similarity computations in z-space to determine the similarity degree between any two temporal patterns. The interest measure is designed by considering z-values between z = 0 and z = 3.09. Applying the Krishna Sudarsana requires moving the threshold value given by user to a different transformation space which is a defined as a function of standard deviation. In addition to proposing interest measure, new expressions for standard deviation and equivalent z-space threshold are derived for similarity computations. For experimental evaluation, we considered Naïve, Sequential and Spamine algorithms that applies Euclidean distance function and compared performance of these three approaches to Z-Spamine algorithm that uses Krishna Sudarsana by choosing various test cases. Experiment results proved the performance of the proposed approach is better to Sequential approach that uses snapshot database scan strategy and Spamine approach that uses lattice based database scan strategy. (shrink)

Discovery of temporal association patterns, temporal association rules from temporal databases is extensively studied by academic research community and applied in various industrial applications. Temporal association pattern discovery is extended to similarity based temporal association pattern discovery from time-stamped transaction datasets by researchers Yoo and Sashi Sekhar. They introduced methods for pruning through distance bounds, and have also introduced SEQUENTIAL and SPAMINE algorithms for pattern mining that are based on snapshot data scan and lattice data scan strategies respectively. Our previous (...) research introduced algorithms G-SPAMINE, MASTER, Z-SPAMINE for time profiled association pattern discovery. These algorithms applied distance measures SRIHASS, ASTRA, and KRISHNA SUDARSANA for similarity computations. SEQUENTIAL, SPAMINE, G-SPAMINE, MASTER, Z-SPAMINE approaches are all based on snapshot and lattice database scan strategies and prunes temporal itemsets by making use of lower bound, upper bound support time sequences and upper-lower distance bound, lower bound distance values. The major limitation of all these algorithms is their inevitability to eliminate dataset scanning process for knowing true supports of itemsets and essential need to have dataset available in memory. To eliminate the requirement of retaining dataset in main memory, algorithms VRKSHA and GANDIVA are two pioneering research contributions that introduced tree structure for time profiled temporal association mining. VRKSHA is based on snapshot tree scan technique while GANDIVA is a lattice tree scan based approach. VRKSHA and GANDIVA both apply Euclidean distance function, but they do not estimate support and distance bounds. This research introduces the pioneering work ULTIMATE that uses a novel tree structure. The tree is generated using similarity measure ASTRA. ULTIMATE uses support bound and distance bound computations for pruning temporal patterns. Experiment results showed that ULTIMATE outperforms SEQUENTIAL, SPAMINE, G-SPAMINE, MASTER, VRKSHA, GANDIVA algorithms. (shrink)

The book is an introduction to key concepts of Indian Philosophy, seen from the perspective of the influential school of Pr?bh?kara M?m??s? (flourished from the 7th until the 20th c. AD). It includes the edition and translation of R?m?nuj?c?rya's ??straprameyapariccheda.

Rāmānujācārya’s Tantrarahasya, a philosophical treatise mainly dedicated to the hermeneutics and epistemology of the Pūrva Mīmāṃsā School, might be considered hardly more than a jigsaw of reused passages, since one third of it has a direct source, and a further third has its roots in interlanguage usage. It is thus a perfect case study for investigating the compositional habits of philosophical authors in pre-modern śāstra literature. The article analyses the formal aspects of textual reuse by Rāmānujācārya and draws some general (...) conclusions regarding the author’s intellectual affiliation to Prābhākara Mīmāṃsā on the basis of the way he reuses his school’s texts as compared to the reuse of other schools’ texts. A final section discusses the possibility of generalising these results to the approach to textual reuse of the whole Pūrva Mīmāṃsā. (shrink)