Efficient Time Series Clustering and Its Application to Social Network Mining

Journal of Intelligent Systems 23 (2):213-229 (2014)

Abstract
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.
Keywords No keywords specified (fix it)
Categories No categories specified
(categorize this paper)
ISBN(s)
DOI 10.1515/jisys-2014-0005
Options
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

Our Archive


Upload a copy of this paper     Check publisher's policy     Papers currently archived: 41,524
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

No references found.

Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

Single-Valued Neutrosophic Minimum Spanning Tree and Its Clustering Method.Jun Ye - 2014 - Journal of Intelligent Systems 23 (3):311-324.
Classification System for Serial Criminal Patterns.Kamal Dahbur & Thomas Muscarello - 2003 - Artificial Intelligence and Law 11 (4):251-269.
Event Mining Through Clustering.T. V. Geetha & E. Umamaheswari - 2014 - Journal of Intelligent Systems 23 (1):59-73.
Research on Context-Awareness Mobile SNS Recommendation Algorithm.Zhijun Zhang & Hong Liu - 2015 - Pattern Recognition and Artificial Intelligence 28.
The Time of Our Lives.David Hugh Mellor - 2001 - Royal Institute of Philosophy Supplement 48:45-59.

Analytics

Added to PP index
2017-01-12

Total views
8 ( #798,681 of 2,248,816 )

Recent downloads (6 months)
4 ( #484,078 of 2,248,816 )

How can I increase my downloads?

Downloads

My notes

Sign in to use this feature