Name
Abstract | ||
|---|---|---|
We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster colonies' to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/a11100151 | ALGORITHMS |
Keywords | Field | DocType |
data mining,clustering analysis,adaptive K-means,simulated annealing | Simulated annealing,Cluster (physics),k-means clustering,Sample (statistics),Algorithm,Artificial intelligence,Merge (version control),Cluster analysis,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 10 | 1999-4893 |
Citations | PageRank | References |
1 | 0.36 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdel-Rahman Hedar | 1 | 404 | 30.79 |
| Abdel-Monem M. Ibrahim | 2 | 1 | 0.70 |
| Alaa E. Abdel-hakim | 3 | 122 | 9.75 |
| Adel A. Sewisy | 4 | 20 | 4.33 |