To overcome the problems of having a high computation of multi-scale possibilistic clustering algorithm (MPCM),an improved algorithm is proposed in this paper. IMPCM uses the convergent points of k-means to initial-ize MPCM. IMPCM inherits the merits of the MPCM. In the meanwhile,IMPCM solves the problem of MPCM that the initialization points are superfluous and the position is not ideal which make MPCM has too many iteration times. The contrast experiments show that IMPCM has a good clustering results and high computational efficiency.%针对多尺度可能性聚类算法(MPCM)计算复杂度较高的问题,提出一种改进的多尺度可能性聚类算法(IMPCM).算法利用k-均值聚类的收敛点来作为MPCM的初始点,在继承了MPCM优点的同时,解决了原始MPCM中无效初始点过多以及初始点位置不理想造成的迭代次数过高的问题.对比实验结果表明,算法具有良好的聚类效果与更高的计算效率.
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