Multi-view clustering of web documents using multi-objective genetic algorithm
Abdul Wahid, Xiaoying Gao, Peter M. Andreae · 2014
Clustering ensembles are a common approach to clustering problem, which combine a collection of clustering into a superior solution. The key issues are how to generate different candidate solutions and how to combine them. Common approach for generating candidate clustering solutions ignores the multiple representations of the data (i.e., multiple views) and the standard approach of simply selecting the best solution from candidate clustering solutions ignores the fact that there may be a set of clusters from different candidate clustering solutions which can form a better clustering solution. This paper presents a new clustering method that exploits multiple views to generate different clustering solutions and then selects a combination of clusters to form a final clustering solution. Our method is based on Nondominated Sorting Genetic Algorithm (NSGA-II), which is a multi-objective optimization approach. Our new method is compared with five existing algorithms on three data sets that have increasing difficulty. The results show that our method significantly outperforms other methods.