Study on subspace Clustering Algorithm
Huiping Li · Advanced science and technology letters · 2014
For the fact that telecom data size is extremely huge and the management is much complicated, the paper proposes subspace clustering algorithm based on multi-rule constraint, to mine business knowledge information in a more efficient and accurate manner. By relying on K-means clustering algorithm, the method improves selection and mutation operation of genetic algorithms and thus corrects inappropriate choice of K-means initial clustering centers. Meanwhile, with the use of variable weighting strategy, data classification sparseness in the clustering is overcome. A fast and useful mining method is enabled for massive data. Results show its better performance in terms of computing efficiency, accuracy and ability.