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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Binarizing Change for Fast Trend Similarity Based Clustering of Time Series Data
verfasst von : Ibrahim K. A. Abughali, Sonajharia Minz
Erschienen in: Pattern Recognition and Machine Intelligence
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Abstract
It is observed that traditional clustering methods do not necessarily perform well on time-series data because of the temporal relationships in the observed values over a period of time. Another issue with time series is that databases contain bulk amount of data in terms of dimension and size. Clustering algorithms based on traditional measures of dissimilarity find trade-offs between efficiency and accuracy. In addition, time series analysis should be more concerned with the patterns in change and the points of change rather than the values of change. In this paper a new representation technique and similarity measure have been proposed for agglomerative hierarchical clustering.