A New Approach to Present Prototypes in Clustering of Time Series

There are considerable advances in clustering time series data in data mining concept. However, most of which use traditional approaches and try to customize the algorithms to be compatible with time series data. One of the significant problems with traditional clustering is defining prototype specially in partitional clustering where it needs centroids as representative of each cluster. In this paper we present a novel effective approach to define the prototypes based on time series nature. The prototype is constructed based on fuzzy concept efficiently. Moreover, it is demonstrated how the prototypes are moved in iterations. We will present the benefits of the proposed prototype by implementing a real application: Customer transactions clustering.

[1]  Vincent S. Tseng,et al.  A novel two-level clustering method for time series data analysis , 2010, Expert Syst. Appl..

[2]  津本 周作,et al.  Empirical Comparison of Clustering Methods for Long Time-Series Databases (小特集 「アクティブマイニング」および一般) , 2003 .

[3]  Jorma Laaksonen,et al.  A comparison of techniques for automatic clustering of handwritten characters , 2002, Object recognition supported by user interaction for service robots.

[4]  Georg Dorffner,et al.  Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams. Lessons learned from financial volatility trading. , 2000 .

[5]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[6]  Dragomir Anguelov,et al.  Mining The Stock Market : Which Measure Is Best ? , 2000 .

[7]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[8]  Tak-Chung Fu,et al.  Pattern discovery from stock time series using self-organizing maps , 2016 .

[9]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[10]  Mohan M. Trivedi,et al.  Learning trajectory patterns by clustering: Experimental studies and comparative evaluation , 2009, CVPR.

[11]  Earl Cox,et al.  Fuzzy Modeling And Genetic Algorithms For Data Mining And Exploration , 2005 .

[12]  Eamonn J. Keogh,et al.  Iterative Deepening Dynamic Time Warping for Time Series , 2002, SDM.

[13]  Karsten P. Ulland,et al.  Vii. References , 2022 .

[14]  Tieniu Tan,et al.  Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[15]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[16]  Xiaoming Jin,et al.  Indexing and Mining of the Local Patterns in Sequence Database , 2002, IDEAL.

[17]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[18]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

[19]  David Sankoff,et al.  Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison , 1983 .

[20]  James C. Bezdek,et al.  Fuzzy mathematics in pattern classification , 1973 .

[21]  Michalis Vazirgiannis,et al.  Clustering algorithms and validity measures , 2001, Proceedings Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001.

[22]  Shusaku Tsumoto,et al.  Empirical Comparison of Clustering Methods for Long Time-Series Databases , 2003, Active Mining.

[23]  Pasi Fränti,et al.  Time-series clustering by approximate prototypes , 2008, ICPR.

[24]  V. Kavitha,et al.  Clustering Time Series Data Stream - A Literature Survey , 2010, ArXiv.

[25]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[26]  Kilian Stoffel,et al.  Classification Rules + Time = Temporal Rules , 2002, International Conference on Computational Science.