关键词: 多视角数据, 模糊聚类, 熵加权, 中心化策略

Abstract: This paper deals with clustering for multi-view data. Multi-view clustering takes every data view into consideration, as more effective information can be used in the clustering process, its performance is better than that of the single-view clustering algorithm. However, most existing clustering algorithms consider every view equally in terms of importance, but if there is a poor quality view, it will seriously affect the clustering results. Due to differences in the quality of information that different views contain, the impacts on the clustering results are also different. This paper assigns different weights for different views according to their contribution rate on clustering, and proposes a new multi-view clustering algorithm — EWCMVC (entropy weighting centralized multi-view fuzzy clustering) by using the centralized strategy. The experimental results obtained both on synthetic and real datasets show that the proposed algorithm outperforms the most of existing multi-view clustering algorithms.

Key words: multi-view datasets, fuzzy clustering, entropy weighting, centralized strategy