Randomized algorithms
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Cure: an efficient clustering algorithm for large databases
Information Systems
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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This paper presents a clustering algorithm named HYBRID. HYBRID has two phases: in the first phase, a set of spherical atom-clusters with same size is generated, and in the second phase these atom-clusters are merged into a set of molecule-clusters. In the first phase, an incremental clustering method is applied to generate atom-clusters according to memory resources. In the second phase, using an edge expanding process, HYBRID can discover molecule-clusters with arbitrary size and shape. During the edge expanding process, HYBRID considers not only the distance between two atom-clusters, but also the closeness of their densities. Therefore HYBRID can eliminate the impact of outliers while discovering more isomorphic molecule-clusters. HYBRID has the following advantages: low time and space complexity, no requirement of users' involvement to guide the clustering procedure, handling clusters with arbitrary size and shape, and the powerful ability to eliminate outliers.