Name
Title | ||
|---|---|---|
Clustering by integrating multi-objective optimization with weighted k-means and validity analysis |
Abstract | ||
|---|---|---|
This paper presents a clustering approach that integrates multi-objective optimization, weighted k-means and validity analysis in an iterative process to automatically estimate the number of clusters, and then partition the whole given data to produce the most natural clustering. The proposed approach has been tested on real-life dataset; results of both weighted and unweighed k-means are reported to demonstrate applicability and effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11875581_55 | IDEAL |
Keywords | Field | DocType |
iterative process,multi-objective optimization,clustering approach,unweighed k-means,real-life dataset,validity analysis,weighted k-means,natural clustering,multi objective optimization,k means | k-means clustering,Fuzzy clustering,Pattern recognition,Iterative and incremental development,Correlation clustering,Computer science,Multi-objective optimization,Artificial intelligence,Partition (number theory),Cluster analysis,Data partitioning,Machine learning | Conference |
Volume | ISSN | ISBN |
4224 | 0302-9743 | 3-540-45485-3 |
Citations | PageRank | References |
9 | 0.54 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tansel Özyer | 1 | 196 | 23.30 |
| Reda Alhajj | 2 | 1919 | 205.67 |
| Ken Barker | 3 | 834 | 83.23 |