Name
Title | ||
|---|---|---|
U-Control Chart Based Differential Evolution Clustering For Determining The Number Of Cluster In K-Means |
Abstract | ||
|---|---|---|
The automatic clustering differential evolution (ACDE) is one of the clustering methods that are able to determine the cluster number automatically. However, ACDE still makes use of the manual strategy to determine k activation threshold thereby affecting its performance. In this study, the ACDE problem will be ameliorated using the u-control chart (UCC) then the cluster number generated from ACDE will be fed to k-means. The performance of the proposed method was tested using six public datasets from the UCI repository about academic efficiency (AE) and evaluated with Davies Bouldin Index (DBI) and Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-19223-5_3 | GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2019 |
Keywords | Field | DocType |
K-means, Automatic clustering, Differential evolution, K activation threshold, U control chart, Academic efficiency (AE) | k-means clustering,Data mining,Discrete mathematics,Cosine similarity,Davies–Bouldin index,Computer science,Determining the number of clusters in a data set,Differential evolution,Control chart,Chart,Cluster analysis | Conference |
Volume | ISSN | Citations |
11484 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús Silva | 1 | 0 | 3.38 |
| Omar Bonerge Pineda Lezama | 2 | 0 | 1.35 |
| Noel Varela | 3 | 0 | 1.01 |
| Jesús García Guiliany | 4 | 0 | 0.34 |
| Ernesto Steffens Sanabria | 5 | 0 | 0.34 |
| Madelin Sánchez Otero | 6 | 0 | 0.34 |
| Vladimir Álvarez Rojas | 7 | 0 | 0.34 |