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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Iam-On, N. Boongoen, T. |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Information Technology, Mae Fah Luang University, Chiang Rai 57100, Thailand (Iam-On, N.) || Department of Mathematics and Computer Science, Royal Thai Air Force Academy, Bangkok 10220, Thailand (Boongoen, T.) |
| Abstract | Cancer has been identified as the leading cause of death. It is predicted that around 20–26 million people will be diagnosed with cancer by 2020. As a result, there is an urgent need for a more effective methodology to prevent and cure cancer. Microarray technology provides a useful basis of achieving this ultimate goal. For cancer research, it has become almost routine to create gene expression profiles, which can discriminate patients into good and poor prognosis groups. This cluster analysis offers a useful basis for individualized treatment of disease. Cluster ensembles have been shown to be better than any standard clustering algorithm for such a task. 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. Among different state-of-the-art methods, the link-based approach (LCE) provides a highly accurate clustering. This paper presents the improvement of LCE with a new link-based similarity measure being developed and engaged. Additional information that is already available in an information 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 published microarray datasets, in comparison with the original LCE and several well-known cluster ensemble techniques. The findings suggest that the new model can improve the accuracy of LCE and performs better than the others investigated in the empirical study. |
| Starting Page | 2014 |
| Ending Page | 2019 |
| File Size | 207882 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467317139 |
| e-ISBN | 9781467317146 |
| e-ISBN | 9781467317122 |
| DOI | 10.1109/ICSMC.2012.6378034 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-14 |
| Publisher Place | Korea (South) |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Clustering algorithms Indexes Cancer Gene expression Standards Accuracy Clustering methods link-based similarity microarray data clustering cluster ensembles |
| Content Type | Text |
| Resource Type | Article |
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