Improved density-dased clustering algorithm based on information entropy and ant colony optimization abstract - Yong-hua ZHANG; Fei-ming DU; Dai-wen WU (Tongxin xuebao)
An integration of clustering algorithm was proposed for the shortage of the DBSCAN algorithm in inhomogeneous distribution and large-scale data processing.The algorithm extracted representative data from the original data set using information entropy and ant colony clustering technology,and did DBSCAN clustering based on the representative data subset.The experiment show that this algorithm is effective to reduce the I/O-consuming and memory requirements,and resolve the cluster problem of large-scale data sets containing categories property.