Clustering for Complex and Massive Data

For applications of clustering algorithms, the key techniques are to handle complicatedly distributed clusters and process massive data effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper, which can handle clusters of arbitrary shapes, sizes and densities. For very large databases, such as spatial database and multimedia database, the traditional clustering algorithms are of limitations in validity and scalability. According to the notion of clustering feature of BIRCH, an incremental clustering algorithm is designed and implemented, which solves the problems of effectiveness, space and time complexities of clustering algorithms for very large spatial databases.