Name
Abstract | ||
|---|---|---|
Clustering is an unsupervised process since there are no predefined classes and no examples that would indicate grouping properties in the data set. The majority of the clustering algorithms behave differently depending on the features of the data set and the initial assumptions for defining groups. Therefore, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity. Evaluating and assessing the results of a clustering algorithm is the main subject of cluster validity. In this paper we present a review of the clustering validity and methods. More specifically, Part I of the paper discusses the cluster validity approaches based on external and internal criteria. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1145/565117.565124 | SIGMOD Record |
Keywords | Field | DocType |
internal criterion,initial assumption,clustering validity,clustering scheme,cluster validity,defining group,clustering algorithm,grouping property,main subject,cluster validity method | Data mining,Fuzzy clustering,CURE data clustering algorithm,Clustering high-dimensional data,Correlation clustering,Access method,Computer science,sort,FLAME clustering,Cluster analysis,Database | Journal |
Volume | Issue | ISSN |
31 | 2 | 0163-5808 |
Citations | PageRank | References |
201 | 10.73 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Halkidi | 1 | 1304 | 72.90 |
| Yannis Batistakis | 2 | 975 | 50.28 |
| Michalis Vazirgiannis | 3 | 3942 | 268.00 |