Incremental clustering algorithm via crossentropy

A new incremental clustering method is presented, which partitions dynamic data sets by mapping data points in high dimension space into low dimension space based on (fuzzy) crossentropy(CE). This algorithm is divided into two parts: initial clustering process and incremental clustering process. The former calculates fuzzy crossentropy or crossentropy of one point relative to others and a hierachical method based on crossentropy is used for clustering static data sets. Moreover, it has the lower time complexity. The latter assigns new points to the suitable cluster by calculating membership of data point to existed centers based on the crossentropy measure. Experimental comparisons show the proposed method has lower time complexity than common methods in the largescale data situations or dynamic work environments.