Authors: Natthakan Iam-On; Tossapon Boongoen; Nattawut Kongkotchawan
Addresses: School of Information Technology, Mae Fah Luang University, 333 Moo1, Tasud, Muang Chiang Rai 57100, Thailand ' Department of Mathematics and Computer Science, Royal Thai Air Force Academy, 171/1 Klongtanhon, Saimai, Bangkok 10220, Thailand ' Department of Mathematics and Computer Science, Royal Thai Air Force Academy, 171/1 Klongtanhon, Saimai, Bangkok 10220, Thailand
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.
Keywords: data clustering; ensemble clustering; cancer microarrays; link-based similarity; microarray data analysis; cluster ensembles.
International Journal of Collaborative Intelligence, 2014 Vol.1 No.1, pp.45 - 67
Received: 10 Apr 2013
Accepted: 19 Apr 2013
Published online: 27 Sep 2014 *