NDLI: An Improved Method for Clustering Gene Microarray Data Based on Intra-Cluster Distance and Variance

Content Provider	IEEE Xplore Digital Library
Author	Bhattacharjee, K. Chatterjee, S. Konar, A. Janarthanan, R.
Copyright Year	2009
Description	Author affiliation: Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India., soumyachat@gmail.com (Chatterjee, S.) \|\| Department of Information Technology, Jaya Engineering College, Chennai, India. srmjana_73@yahoo.com (Janarthanan, R.) \|\| Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India., kasturibhattacharjee@yahoo.com (Bhattacharjee, K.) \|\| Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India., konaramit@yahoo.co.in (Konar, A.)
Abstract	A major use of microarray data is to classify genes with similar expression profiles into groups in order to investigate their biological significance. Cluster analysis is by far the most used technique for gene expression analysis. It has grown to be an important research topic in a wide variety of fields owing to its wide applications. A number of clustering methods exist with one or more limitations, such as, dependence on initial parameters, inefficiency in presence of noisy data, to name a few. This paper proposes a novel clustering algorithm for gene microarray data which is free from the above limitations. Besides, it is simple to implement, and is has been proved to be very effective even in the presence of noisy data. Further, it is extremely exhaustive and is hence, less likely to get stuck at local optima.
Starting Page	20
Ending Page	25
File Size	2278874
Page Count	6
File Format	PDF
ISBN	9781424429271
DOI	10.1109/IADCC.2009.4808973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fungi Clustering methods Clustering algorithms DNA Genomics Data engineering Educational institutions Telecommunication computing Gene expression Bioinformatics
Content Type	Text
Resource Type	Article

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