Review of MIR-max algorithm and potential improvements

Akshyadeep Raghav, Raza Hasan

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review

Abstract

This paper discusses in depth the different parts of the MIR-max clustering algorithm with respect to the problem of diagnosing river quality. An equivalent information theoretic measure is proposed in this paper for clustering which is based on conditional entropy. The original mutual information method of clustering is compared with the proposed conditional entropy of states given to the cluster. This information theoretic concept measures the quality of cluster in terms of uncertainty existing within a cluster. It is found that the measure of conditional entropy is also useful for quantifying the 'fit' of a new sample in a cluster. Indifferent mutual information is also described in the paper. Numeric examples are provided in this paper regarding the feasibility of the proposed measure for the clustering algorithm.
Original languageEnglish
Title of host publication2011 International Conference on Information Management, Innovation Management and Industrial Engineering
PublisherIEEE
Pages554-558
Number of pages5
ISBN (Print)978-1-61284-450-3
DOIs
Publication statusPublished - 29 Dec 2011
Event2011 International Conference on Information Management, Innovation Management and Industrial Engineering - Shenzhen, China
Duration: 26 Nov 201127 Nov 2011

Conference

Conference2011 International Conference on Information Management, Innovation Management and Industrial Engineering
Abbreviated titleICIII
Country/TerritoryChina
CityShenzhen
Period26/11/1127/11/11

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