Dynamic Clustering Of High Speed Data Streams
We consider the problem of clustering data streams. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating but a constant time delay. The purpose of this study is to analyze the working of popular algorithms on clustering data streams and make a comparative analysis.
Keywords: Data streams, Unsupervised learning, Partitional clustering, Hierarchical clustering
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ABOUT THE AUTHORS
J. Chandrika
Department of CS & E, M C E ,Hassan – 573 201
K.R. Ananda Kumar
Department of CS & E, SJBIT , Bangalore – 560 060
J. Chandrika
Department of CS & E, M C E ,Hassan – 573 201
K.R. Ananda Kumar
Department of CS & E, SJBIT , Bangalore – 560 060
