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 language | English |
---|---|
Title of host publication | 2011 International Conference on Information Management, Innovation Management and Industrial Engineering |
Publisher | IEEE |
Pages | 554-558 |
Number of pages | 5 |
ISBN (Print) | 978-1-61284-450-3 |
DOIs | |
Publication status | Published - 29 Dec 2011 |
Event | 2011 International Conference on Information Management, Innovation Management and Industrial Engineering - Shenzhen, China Duration: 26 Nov 2011 → 27 Nov 2011 |
Conference
Conference | 2011 International Conference on Information Management, Innovation Management and Industrial Engineering |
---|---|
Abbreviated title | ICIII |
Country/Territory | China |
City | Shenzhen |
Period | 26/11/11 → 27/11/11 |