Genetic Algorithms for Multi-Criterion Classification and Clustering in Data Mining
Satchidananda Dehuri, Vyasa Vihar, Ashish Kumar Ghosh, Rajib Mall · 2006
This paper focuses on multi-criteria tasks such as classification and clustering in the context of data mining. The cost functions like rule interestingness, predictive accuracy and comprehensibility associated with rule mining tasks can be treated as multiple objectives. Similarly, complementary measures like compactness and connectedness of clusters are treated as two objectives for cluster analysis. We have carried out an extensive simulation for these tasks using different real life and artificially created datasets. Experimental results presented here show that multi-objective genetic algorithms (MOGA) bring a clear edge over the single objective ones in the case of classification task; whereas for clustering task they produce comparable results.