A novel point density based validity index for clustering gene expression datasets Online publication date: Wed, 03-May-2017
by M. Arif Wani; Romana Riyaz
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 17, No. 1, 2017
Abstract: Elucidating the patterns hidden in gene expression data offers an opportunity for identifying co-expressed genes and biologically relevant grouping of genes. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the microarray data. A first step toward addressing this challenge is the use of clustering techniques. Validation of results obtained from a clustering algorithm is an important part of the clustering process. In this paper, we propose a new cluster validity index (ARPoints index) for the purpose of cluster validation. A new approach to determine the compactness measure and distinctness measure of clusters is presented. We revisit commonly known indices and conduct a thorough comparison of these indices with the proposed index and provide a summary of performance evaluation of different indices. Experimental results show that the proposed index performs better than the commonly known cluster validity indices.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook