Pattern-based time-series subsequence clustering using radial distribution functions

Knowledge and Information Systems

Volumn 18, Issue 1, 2009, Pages 1-27

  Denton, Anne M ^a   Besemann, Christopher A ^a   Dorr, Dietmar H ^a  

^a NORTH DAKOTA STATE UNIVERSITY   (United States)

Author keywords

Clustering noisy data; Density based clustering; Noise elimination; Time series labeling; Time series subsequence clustering

Indexed keywords

DISTRIBUTION FUNCTIONS; TIME SERIES;

DENSITY-BASED CLUSTERING; KERNEL DENSITY; NOISE ELIMINATION; NOISY DATA; PATTERN MINING; RADIAL DISTRIBUTION FUNCTIONS; TIME SERIES SUBSEQUENCES; WINDOW SIZE;

CLUSTERING ALGORITHMS;

EID: 58549093382     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0125-7     Document Type: Article

References (35)

1. Berndt D, Clifford J (1996) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, chapter Finding patterns in time series: a dynamic programming approach, pp 229-248
2. Breunig M, Kriegel H-P, Ng R, Sander J (2000) LOF: Identifying density-based local outliers. In: Proceedings of 5th ACM SIGMOD international conference on mangement of data, pp 93-104
3. Chen J (2005) Making subsequence time series clustering meaningful. In: Proceedings of the 5th IEEE international conference on data mining (ICDM'05), Houston, pp 114-121
4. Chen J (2007a) Making clustering in delay-vector space meaningful. Knowl Inf Syst 11(3): 369-385
5. Chen J (2007b) Useful clustering outcomes from meaningful time series clustering. In: Proceedings of the Australasian data mining conference, Gold Coast, Australia
6. Comaniciu D, Meer P (2002) Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603-619
7. Das G, Lin K-I, Mannila H et al (1998) Rule discovery from time series. In: Proceedings of the 4th ACM SIGKDD international conference on data mining, Rio de Janeiro, Brazil
8. Daw C, Finney C, Tracy E (2003) A review of symbolic analysis of experimental data. Rev Sci Instrum 74(2):915-930
9. Denton A (2004) Density-based clustering of time series subsequences. In: Proceedings of the 3rd workshop on mining temporal and sequential data (TDM 04) in conj. with the 10th ACM SIGKDD international conference on knowledge discovery and data mining, Seattle
10. Denton A (2005) Kernel-density-based clustering of time series subsequences using a continuous random-walk noise model. In: Proceedings of the 5th IEEE international conference on data mining (ICDM'05), Houston, pp 122-129
11. Ernst J, Nau G, Bar-Joesph Z (2005) Clustering short time series gene expression data, Bioinformatics 21(Supplement 1)
12. Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining
13. Gavrilov M, Anguelov D, Indyk P, Motwani R (2000) Mining the stock market (extended abstract): Which measure is best?. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston, pp 487-496
14. Goldberger A, Amaral L, Glass L et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation 101(23):e215-e220. Circulation Electronic Pages: [http://circ.ahajournals.org/cgi/content/full/101/23/ e215]
15. Goldin D, Mardales R, Nagy G (2006) In search of meaning for time series subsequence clustering: Matching algorithms based on a new distance measure. In: Proceedings of the Conference on Information and Knowledge Management, Washington, DC
16. Han J, Kamber M (2006) Data Mining: Concepts and Techniques 2nd edn. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers
17. Hinneburg A, Keim D (2003) A general approach to clustering in large databases with noise. Knowl Inf Syst 5(4):387-415
18. Ide T (2006) Why does subsequence time-series clustering produce sine waves?. In: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp 311-322
19. Ihler A (accessed 04/2003), Kernel density estimation toolbox for matlab (r13). [http://ttic.uchicago.edu/∼ihler/code/kde.php]
20. Kantz H, Schreiber T (1999) Nonlinear time series analysis. Cambridge University Press, Cambridge
21. Kaufman L, Rousseeuw P (1990) Finding groups in data: An introduction to cluster analysis. Wiley, New York
22. Keogh E, Folias T (accessed 2003) The ucr time series data mining archive. [http://www.cs.ucr.edu/~eamonn/TSDMA/index.html]
23. Keogh E, Lin J, Truppel W (2003) Clustering of time series subsequences is meaningless: Implications for previous and future research. In: Proceedings of the IEEE international conference on data mining, Melbourne, pp 115-122
24. Keogh E, Pazzani M (2000) Scaling up dynamic time warping for datamining applications. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston, MA, pp 285-289
25. Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing sax: A novel symbolic representation of time series. Data Mining Knowl Discovery 15(2):107-144
26. Ng R, Han J (2002) Clarans: A method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5):1003-1016
27. Papadimitriou S, Kitagawa H, Gibbons P, Faloutsos C (2003) Loci: Fast outlier detection using the local correlation integral. In: Proceedings of the 19th international conference on data engineering (ICDE), pp 315-326
28. Patel P, Keogh E, Lin J, Lonardi S (2002) Mining motifs in massive time series databases. In: Proceedings of the IEEE international conference on data mining, Maebashi City, Japan
29. Peker K (2005) Subsequence time series (sts) clustering techniques for meaningful pattern discovery. In: Proceedings of the IEEE KIMAS Conference
30. Saito N (1995) Local feature extraction and its application using a library of bases. PhD Thesis
31. Simon G, Lee J, Verleysen M (2005) On the need of unfolding preprocessing for time series clustering. In: Proceedings of Workshop on Self-Organizing Maps (WSOM'05), Paris, France, pp 251-258
32. Verleysen M, François D (2005) The curse of dimensionality in data mining and time series prediction, In: Cabestany J, Prieto A, Sandoval F (eds) Computational Intelligence and Bioinspired Systems, Lecture Notes in Computer Science 3512. Springer, Heidelberg pp 758-770
33. Vlachos M, Gunopoulos D, Kollios G (2002) Discovering similar multidimensional trajectories. In: Proceedings 18th international conference on data engineering (ICDE'02), San Jose
34. Weisstein E (2003) Hypersphere, in MathWorld - A Wolfram Web Resource, [http://mathworld.wolfram.com/Hypersphere.html]
35. Yankov D, Keogh E, Medina J et al (2007) Detecting time series motiv under uniform scaling, In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose