Abstract
Clustering is vital in the process of condensing and outlining information, since it can provide a synopsis of the stored data. However, the high dimensionality of multimedia data today presents an insurmountable challenge for clustering algorithms.
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References
Agrawal R, Faloutsos C, Swami A. Efficient similarity search in sequence databases. In: Proceedings of the 4th Int'l Conference on Foundations of Data Organization and Algorithms; 1993 Oct 13–15; Chicago, IL; pp. 69–84.
Bradley P, Fayyad U, Reina C. Scaling clustering algorithms to large databases. In: Proceedings of the 4th Int'l Conference on Knowledge Discovery and Data Mining; 1998 Aug 27–31; New York, NY, pp. 9–15.
Chan K, Fu AW. Efficient time series matching by wavelets. In: Proceedings of the 15th IEEE Int'l Conference on Data Engineering; 1999 Mar 23–26; Sydney, Australia; pp. 126–133.
Chu S, Keogh E, Hart D, Pazzani M. Iterative deepening dynamic time warping for time series. In: Proceedings of the 2nd SIAM International Conference on Data Mining; 2002 Apr 11–13; Arlington, VA.
Ding C, He X, Zha H, Simon H. Adaptive dimension reduction for clustering high dimensional data. In: Proceedings of the 2nd IEEE International Conference on Data Mining; 2002 Dec 9–12; Maebashi, Japan, pp. 147–154.
Daubechies I. Ten Lectures on Wavelets. Number 61 in CBMS-NSF regional conference series in applied mathematics, Society for Industrial and Applied Mathematics; 1992; Philadelphia.
Dumoulin J. NSTS 1988 News Reference Manual. http://www.fas.org/spp/civil/sts/
Faloutsos C, Ranganathan M, Manolopoulos Y. Fast subsequence matching in time-series databases. In: Proceedings of the ACMSIGMOD Int'l Conference on Management of Data; 1994 May 25–27; Minneapolis, pp. 419–429.
Fayyad U, Reina C, Bradley P. Initialization of iterative refinement clustering algorithms. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining; 1998 Aug 27–31; New York, NY; pp. 194–198.
Gibson S, Harvey R. Analyzing and simplifying histograms using scale-trees. In: Proceedings of the 11th Int'l Conference on Image Analysis and Processing; 2001 Sept 26–28; Palermo, Italy.
Grass J, Zilberstein S. Anytime algorithm development tools. Sigart Artificial Intelligence 1996;7:2.
Hearst MA, Pedersen JO. Reexamining the cluster hypothesis: Scatter/gatter on retrieval results. In: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1996 Aug 18–22; Zurich, Switzerland.
Huhtala Y, Kärkkäinen J, Toivonen H. Mining for similarities in aligned time series using wavelets. Data Mining and Knowledge Discovery: Theory, Tools, and Technology. SPIE Proceedings Series 1999; 3695:150–160. Orlando, FL.
Keogh E, Pazzani M. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: Proceedings of the 4th Int'l Conference on Knowledge Discovery and Data Mining; 1998 Aug 27–31; New York, NY; pp. 239–241.
Keogh E, Chakrabarti K, PazzaniM, Mehrotra S. Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of ACM SIGMOD Conference on Management of Data. 2001 May 21–24; Santa Barbara, CA; pp. 151–162.
Keogh E, Folias T. The UCR Time Series Data Mining Archive (http://www.cs.ucr.edu/~eamonn/TSDMA/index.html). 2002.
Korn F, Jagadish H, Faloutsos C. Efficiently supporting ad hoc queries in large datasets of time sequences. In: Proceedings of the ACM SIGMOD Int'l Conference on Management of Data; 1997 May 13–15; Tucson, AZ, pp. 289–300.
McQueen J. Some methods for classification and analysis of multivariate observation. In: Le Cam L, Neyman J, eds. 5th Berkeley Symp. Math. Stat. Prob. 1967; 281–297.
Popivanov I, Miller RJ. Similarity search over time series data using wavelets. In: Proceedings of the 18th Int'l Conference on Data Engineering; 2002 Feb 26-Mar 1; San Jose, CA, pp. 212–221.
Smyth P, Wolpert D. Anytime exploratory data analysis for massive data sets. In: Proceedings of the 3rd Int'l Conference on Knowledge Discovery and Data Mining; 1997 Aug 14–17; Newport Beach, CA, pp. 54–60.
Stehling RO, Nascimento MA, Falcao AX. A compact and efficient image retrieval approach based on border/interior pixel classification. In: Proceedings of the ACMIntl. Conf. on Information and Knowledge Management; 2002 Nov 4–9; McLean, VA.
Struzik Z, Siebes A. The Haar wavelet transform in the time series similarity paradigm. In: Proceedings of Principles of Data Mining and Knowledge Discovery, 3rd European Conference; 1999 Sept 15–18; Prague, Czech Republic; pp. 12–22.
Wu P, Manjunath BS, Newsam S, Shin HD.Atexture descriptor for browsing and similarity retrieval. Journal of Signal Processing: Image Communication 2000;16:1–2;33–43.
Wu Y, Agrawal D, El Abbadi A. A comparison of DFT and DWT based similarity search in time-series databases. In: Proceedings of the 9th ACM CIKM Int'l Conference on Information and Knowledge Management. 2000 Nov 6–11; McLean, VA; pp. 488–495.
Yi B, Faloutsos C. Fast time sequence indexing for arbitrary lp norms. In: Proceedings of the 26th Int'l Conference on Very Large Databases; 2000 Sept 10–14; Cairo, Egypt; pp. 385–394.
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Lin, J., Vlachos, M., Keogh, E., Gunopulos, D. (2007). Multiresolution Clustering of Time Series and Application to Images. In: Petrushin, V.A., Khan, L. (eds) Multimedia Data Mining and Knowledge Discovery. Springer, London. https://doi.org/10.1007/978-1-84628-799-2_4
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DOI: https://doi.org/10.1007/978-1-84628-799-2_4
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