A hybrid genetic algorithm-fuzzy c-means approach for incomplete data clustering based on nearest-neighbor intervals

作者：Li Dan^*; Gu Hong; Zhang Liyong

来源：Soft Computing, 2013, 17(10): 1787-1796.

DOI：10.1007/s00500-013-0997-7

• Fuzzy clustering
• Hybrid approach
• Incomplete data
• Nearest-neighbor interval

Incomplete data are often encountered in data sets used in clustering problems, and inappropriate treatment of incomplete data can significantly degrade the clustering performance. In view of the uncertainty of missing attributes, we put forward an interval representation of missing attributes based on nearest-neighbor information, named nearest-neighbor interval, and a hybrid approach utilizing genetic algorithm and fuzzy c-means is presented for incomplete data clustering. The overall algorithm is within the genetic algorithm framework, which searches for appropriate imputations of missing attributes in corresponding nearest-neighbor intervals to recover the incomplete data set, and hybridizes fuzzy c-means to perform clustering analysis and provide fitness metric for genetic optimization simultaneously. Several experimental results on a set of real-life data sets are presented to demonstrate the better clustering performance of our hybrid approach over the compared methods.

• 出版日期2013-10
• 单位大连理工大学