Clustering from Data Streams
Clustering is the process of grouping objects into different groups, such that the common properties of data in each subset is high, and between different subsets is low. The data stream clustering problem is defined as to maintain a consistent good clustering of the sequence observed so far, using a small amount of memory and time. The issues are imposed by the continuous arriving data points, and the need to analyze them in real time. These characteristics require incremental clustering, maintaining cluster structures that evolve over time. Moreover, the data stream may evolve over time and new clusters might appear, others disappear reflecting the dynamics of the stream.
Partitioning algorithms: construct a partition of a set of objects into k clusters, that minimize some objective function (e.g., the sum of squares distances to the centroid representative). Examples include k-means (Far...
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