A new incremental core-based clustering method

Abstract

Clustering is a data mining activity that aims to differentiate groups inside a given set of objects, with respect to a set of relevant attributes of the analyzed objects. Generally, existing clustering methods, such as k-means algorithm, start with a known set of objects, measured against a known set of attributes. But there are numerous applications where the attribute set characterizing the objects evolves. We propose an incremental, k-means based clustering method, Core Based Incremental Clustering (CBIC), that is capable to re-partition the objects set, when the attribute set increases. The method starts from the partitioning into clusters that was established by applying k-means or CBIC before the attribute set changed. The result is reached more efficiently than running k-means again from the scratch on the feature-extended object set. Experiments proving the method's efficiency are also reported. © 2005 by International Federation for Information Processing.