Bitmap approach to trend clustering for prediction in time series databases

Jong P. Yoon; Yixin Luo; Junghyun Nam

doi:10.1117/12.421085

27 March 2001 Bitmap approach to trend clustering for prediction in time series databases

Jong P. Yoon, Yixin Luo, Junghyun Nam

Proceedings Volume 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III; (2001) https://doi.org/10.1117/12.421085
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

This paper describes a bitmap approach to clustering and prediction of trends in time-series databases. Similar trend patterns, rather than similar data patterns, are extracted from time-series database. We consider four types of matches: (1) Exact match, (2) Similarity match, (3) Exact match by shift, and (4) Similarity match by shift. Each pair of time-series data may be matched in one of these four types if this pair is similar one to another, by similarity (or sim) notion over a threshold. Matched data can be clustered by the same way of matching. To improve performance, we use the notion of center of a cluster. The radius of a cluster is used to determine whether a given time-series data is included in the cluster. We also use a new notion of dissimilarity, called dissim, to make accurate clusters. It is likely that a time-series data is in one cluster rather than in another by using both notions, sim and dissim: a data is similar to one cluster while it is dissimilar to another. For a trend sequence, the cluster that is dissimilar to that sequence is called dissimilar- cluster. The contribution of this paper includes (1) clustering by using not only similarity match but also dissimilarity match. In this way we prevent any positive and negative failures. (2) Prediction by using not only similar trend sequences but also dissimilar trend sequences. (3) A bitmap approach can improve performance of clustering and prediction.

Citation Download Citation

Jong P. Yoon, Yixin Luo, and Junghyun Nam "Bitmap approach to trend clustering for prediction in time series databases", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421085

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