A new link-based method to ensemble clustering and cancer microarray data analysis

Abstract

Ensemble clustering or cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy. This meta-learning formalism helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique, given a set of data. It has proven effective for many problem domains, especially microarray data analysis. Among different state-of-the-art methods, the link-based approach (LCE) recently introduced by Iam-On et al. (2011) provides a highly accurate clustering. This paper presents the improvement of LCE with a new link-based metric being developed and engaged. Additional information that is already available in a network is included in the similarity assessment. As such, this refinement can increase the quality of the measures, hence the resulting cluster decision. The performance of this improved LCE is evaluated on synthetic and UCI benchmark datasets, in comparison with the original and several well-known cluster ensemble techniques. In addition, the application of improved LCE to microarray data analysis is also empirically assessed. The findings suggest that the new model can improve the accuracy of LCE and performs better than the others investigated in this study.

Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient

“…Huang [6] proposed a k-prototypes algorithm for clustering mixed-type data, which combines the ideas of k-means algorithm [2] and k-modes algorithm [3]. e k-prototypes algorithm divides the dataset into k(k ∈ N + ) different subclusters to minimize the value of the Cost Function.…”

“…Clustering aims to find out the correlation between subclusters in datasets and to evaluate the dissimilarity among data objects in these subclusters [2]. In the field of Categorical Data clustering, the classical k-modes algorithm [3] uses the modes vector to represent the Cluster Centers.…”