Name
Abstract | ||
|---|---|---|
This paper proposes a multiple criteria decision making (MCDM)-based framework to address two fundamental issues in cluster validation: 1) evaluation of clustering algorithms and 2) estimation of the optimal cluster number for a given data set. Since both issues involve more than one criterion, they can be modeled as multiple criteria decision making (MCDM) problems. The proposed framework is examined by an experimental study. The results suggest that MCDM methods are practical tools for the evaluation of clustering algorithms. In addition, the selected MCDM method, PROMETHEE II can estimate the optimal numbers of clusters for ten out of fifteen datasets by adjusting the weights of criteria. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.procs.2012.04.140 | PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2012 |
Keywords | Field | DocType |
clustering algorithm, cluster validation, multiple criteria decision making (MCDM) | Data mining,Cluster (physics),Weighted product model,Mathematical optimization,Multiple criteria,Multiple-criteria decision analysis,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,Machine learning | Journal |
Volume | ISSN | Citations |
9 | 1877-0509 | 1 |
PageRank | References | Authors |
0.38 | 13 | 5 |