NDLI: Multi-objective clustering of tissue samples for cancer diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Acharya, S. Thadisina, Y. Saha, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Patna, Patna, India (Acharya, S.; Thadisina, Y.; Saha, S.)
Abstract	In the field of pattern recognition, the study of the gene expression profiles for different tissue samples over different experimental conditions has became feasible with the arrival of micro-array based technology. In cancer research, classification of tissue samples is necessary for cancer diagnosis, which can be done with the help of micro-array technology. In this article we have presented a multi-objective optimization ( MOO ) based clustering technique utilizing AMOSA ( Archived Multi-Objective Simulated Annealing ) as the underlying optimization strategy for classification of tissue samples from cancer data sets. As objective functions three cluster validity indices namely, XB, PBM, and FCM indices are optimized simultaneously to form more accurate clusters of tissue samples. The presented clustering technique is evaluated for two open source benchmark cancer data sets, which are Brain tumor data set and Adult Malignancy data set. In order to evaluate the quality or goodness of produced clusters two cluster quality measures viz, Adjusted Rand Index ( ARI ) and Classification Accuracy ( %CoA ) are calculated for each data set. Comparative results of the presented clustering algorithm with 10 state-of-the-art existing single-objective, multi-objective based clustering algorithms are shown for two benchmark data sets.
Starting Page	1059
Ending Page	1064
File Size	201907
Page Count	6
File Format	PDF
ISBN	9781479930784
e-ISBN	9781479930807
DOI	10.1109/ICACCI.2014.6968235
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-24
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Clustering algorithms Linear programming Indexes Optimization Cancer Tumors
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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