Abstract:

Most existing high-attribute dimensional sparse clustering algorithms can only process binary data and are insufficient in evaluating clustering results,which limits their applications. In order to solve this problem,a noval high-attribute dimensional sparse clustering algorithm based on knowledge granularity is proposed. In this algorithm,first,a semi-fuzzy clustering algorithm is persented to discretize sparse data,with which the sparse similarity and the initial equivalence relation are defined. Then,a precision-variable quadratic clustering model is established to refine the results and further to improve the noise resistance of the proposed algorithm. Finally,an applicationoriented evaluation model of clustering quantity is defined. Test results show that the proposed algorithm is suitable for various granularities and helps to obtain high-accuracy of results of reflecting data characteristics.

Key words: knowledge granularity, high-attribute dimensional sparse data, initial equivalence relation, indiscernibility degree, clustering quality evaluation