A Semi-supervised Incremental Clustering Algorithm for Streaming Data

Nowadays many applications need to deal with evolving data streams . In this work, we propose an incremental clustering approach for the exploitation of user constraints on data streams. Conventional constraints do not make sense on streaming data, so we extend the classic notion of constraint set into a constraint stream . We propose methods for using the constraint stream as data items are forgotten or new items arrive. Also we present an on-line clustering approach for the cost-based enforcement of the constraints during cluster adaptation on evolving data streams. Our method introduces the concept of multi-clusters (m-clusters) to capture arbitrarily shaped clusters. An m-cluster consists of multiple dense overlapping regions, named s-clusters, each of which can be efficiently represented by a single point. Also it proposes the definition of outliers clusters in order to handle outliers while it provides methods to observe changes in structure of clusters as data evolves.