关键词: 聚类, 粗糙集, k-means算法, 混合属性

Abstract: Objects are strictly divided into clusters in the conventional algorithms; however, most of the time, the object boundary cannot be strictly classified. The rough set based k-means clustering algorithm and leader clustering algorithm divide the data object into a clusters upper-bound or lower-bound using rough set, which provides a new perspective of dealing with uncertainty and solve the problem of uncertain boundary region. The problem is that both of the two algorithms cannot deal with mixed valued data, and clustering results significantly depend on the initial value. A definition of the distance for mixed valued data was introduced in this paper, an improved method was put forward for the selection of the initial value, and a clustering algorithm for mixed valued data based on rough set was given. Finally, a simulation experiment was carried out. Simulation results show, under the uncertain situation of cluster number,the clustering accuracy of the algorithm is significantly improved than the traditional k-means algorithm.

Key words: clustering, rough set, k-means algorithm, mixed attribute