Making clustering in delay-vector space meaningful

Knowledge and Information Systems

Volumn 11, Issue 3, 2007, Pages 369-385

  Chen, Jason R ^a  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Clustering; Delay space; Sequential time series clustering; STS clustering; Time series

Indexed keywords

CLUSTERING ALGORITHMS; DYNAMICAL SYSTEMS; TIME SERIES; VECTORS;

CLUSTERING; DELAY SPACES; DISTANCE MEASURE; EUCLIDEAN DISTANCE; IMPORTANT FEATURES; STS CLUSTERING; TIME SERIES CLUSTERING; TIME-SERIES DATA;

VECTOR SPACES;

EID: 34147140547     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-006-0042-6     Document Type: Article

References (22)

1. Babcock B, Datar M, Motwani R, O'Callaghan L (2003) Maintaining variance and k-medians over data stream windows. In: Proceedings of the 22nd symposium on principles of database systems (PODS 2003), San Diego, CA
2. Berkhin P (2002) Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA, http://citeseer.nj.nec.com/ berkhin02survey.html
3. Cotofrei P (2002) Statistical temporal rules. In: Proceedings of the 15th conference on computational statistics, Berlin, Germany
4. Cotofrei P, Stoffel K (2002) Classification rules + time = temporal rules. In: Proceedings of the 2002 international conference on computational science, Amsterdam
5. Das G, Lin K, Mannila H, Renganathan G, Smyth P (1998) Rule discovery from time series. In: Proceedings of the 4th international conference on knowledge discovery and data mining, New York, NY
6. Feng X, Huang H (2005) A fuzzy-set-based reconstructed phase space method for identification of temporal patterns in complex time series. Trans Knowl Data Eng 17(5):601-612
7. Harms SK, Deogun J, Tadesse T (2002) Discovering sequential association rules with constraints and time lags in multiple sequences. In: Proceedings of the 13th international symposium on methodologies for intelligent systems, Lyon, France
8. Harms SK, Reichenbach S, Goddard SE, Tadesse T, Waltman WJ (2002) Drought mining in a geospatial decision support system for drought risk management. In: Proceedings of the 1st national conference on digital government, Los Angeles, CA
9. Hetland ML, Saetrom P (2002) Temporal rules discovery using genetic programming and specialized hardware. In: Proceedings of the 4th international conference on recent advances in soft computing, Nottingham, UK
10. Jin X, Wang L, Lu Y, Shi C (2002) Indexing and mining of the local patterns in sequence database. In: Proceedings of the 3rd international conference on intelligent data engineering and automated learning, Manchester, UK
11. Kantz H, Schreiber T (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge
12. Keogh E, Lin J, Truppel W (2003) Clustering of time series subsequences is meaningless: Implications for previous and future research. In: Proceedings of the international conference of data mining, Melbourne, FL, USA, 19-22
13. Li C, Yu PS, Castelli V (1998) Malm: A framework for mining sequence database at multiple abstraction levels. In: Proceedings of the 7th ACM CIKM international conference on information and knowledge management, Bethesda, MD
14. Mori T, Uehara K (2001) Extraction of primitive motion and discovery of association rules from human motion. In: Proceedings of the 10th IEEE international workshop on human and robot communication, Bordeaux-Paris, France
15. Oates T (1999) Identifying distinctive subsequences in multivariate time series by clustering. In: Proceedings of the international conference on knowledge discovery and data mining, San Diego, CA, USA, 1999, pp 322-326
16. Osaki R, Shimada M, Uehara K A motion recognition mehtod by using primitive motions. In: Arisawa H, Catarici T (eds) Advances in visual information management, visual database systems. Kluwer, 2000, pp 117-127
17. Radhakrishnan N, Wilson JD, Loizou PC (2000) An alternate partitioning technique to quantify the regularity of complex time series. Int J Birfurcat Chaos 10(7):1773-1779
18. Roddick JF, Spiliopoulou M (2002) A survey of temporal knowledge discovery paradigms and methods. Trans Knowl Data Eng 14 (4):750-767
19. Schittenkopf C, Tino P, Dorffner G (2000) The benefit of information reduction for trading strategies. Report series for adaptive information systems and management in economics and management science. Report No. 45
20. Takens F (1981) Detecting strange attractors in turbulence. In: Lecture notes in Mathematics, vol 898. Springer, New York
21. Tino P, Schittenkopf C, Dorffner G (2000) Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams: Lessons learned from financial volatility trading. Report series for adaptive information systems and management in economics and management science. Report No. 45
22. Yairi T, Kato Y, Hori K (2003) Fault detection by mining association rules in house-keeping data. In: Proceedings of the 6th international symposium on artificial intelligence, robotics and automation in space, Montreal