Study on Integrative Clustering of Multiple Genomic Data to Discover Cancer Subtypes.

Date of Submission

December 2014

Date of Award

Winter 12-12-2015

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Machine Intelligence Unit (MIU-Kolkata)


Maji, Pradipta (MIU-Kolkata; ISI)

Abstract (Summary of the Work)

With the advancement of technology, different sources of genetic information become available with a low cost. In the research for finding cancer subtypes, what will help to proceed with a targeted treatment, this opened up a new dimension. However, the basic problem is how to reach towards a proper integration scheme such that both the personal significance and interactive information is conserved, because only then it will be possible to utilize the data resource and obtain richer information about subtypes. On the other hand, as the subtypes are not always properly defined or even known, thus any solution should be unsupervised in nature. This study presented an integration scheme based on the concept of iCluster method, to address these issues. With its many merits, however the crisp nature of clusters obtained by iCluster is not always natural in the case of overlapping and incomplete nature of the data, thus a rough-fuzzy clustering approach will be more suitable, where an addition of intelligent initial center selection algorithm is most desired. A variety of cluster validation index are used to support the claims and present the findings on two different cancer data.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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