Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Requirements for clustering data streams
ACM SIGKDD Explorations Newsletter
Scaling up Dynamic Time Warping to Massive Dataset
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Clustering of time series data-a survey
Pattern Recognition
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In proportion to the rapid development of information technology, time series are today accumulated in finance, medicine, industry and so forth. Therefore, an analysis of them is an urgent need for these applications. As solving these problems clustering time series has much been paid attention. The similarity for the clustering is commonly measured with Euclidean distance and dynamic time warping. In this paper we propose an innovative and novel algorithm for clustering multivariate time series. The algorithm is called "Spider Algorithm". We experimentally show that the similarity from spider algorithm is superior to Euclidean distance or warping path on dynamic time warping, especially when many clusters exist.