Incremental clustering algorithm via cross-entropy

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) cross-entropy(CE). This algorithm is divided into two parts: initial clustering process and incremental clustering process. The former calculates fuzzy cross-entropy or cross-entropy of one point relative to others and a hierachical method based on cross-entropy 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 cross-entropy measure. EjqDerimental comparisons show the proposed method has lower time complexity than common methods in the large-scale data situations or dynamic work environments.