Name
Abstract | ||
|---|---|---|
In the classical k-means algorithm, the value of k must be confirmed in advance. It is difficult to confirm accurately the value of k in reality. This paper proposes an improved genetic k-means algorithm (IGKM) and constructs a fitness function defined as a product of three factors, maximization of which ensures the formation of a small number of compact clusters with large separation between at least two clusters. At last, two artificial and three real-life data sets are considered for experiments that compare IGKM with kmeans algorithm, GA-based method and genetic kmeans algorithm (GKM) by inter-cluster distance (ITD), inner-cluster distance(IND) and rate of separation exactness. The experiments show that IGKM can automatically reach the optimal value of k with high accuracy. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICDMW.2006.30 | Mathematics in Practice and Theory |
Keywords | Field | DocType |
optimal value,large separation,ga-based method,genetic algorithm,genetic kmeans algorithm,improved genetic k-means algorithm,inner-cluster distance,inter-cluster distance,k-means algorithm,separation exactness,classical k-means algorithm,cluster,optimal clustering,kmeans algorithm,data mining,k means algorithm,genetics,fitness function | k-medians clustering,Canopy clustering algorithm,Data mining,CURE data clustering algorithm,Correlation clustering,Determining the number of clusters in a data set,Fitness function,FSA-Red Algorithm,Artificial intelligence,k-medoids,Machine learning,Mathematics | Conference |
Issue | ISBN | Citations |
08 | 0-7695-2702-7 | 2 |
PageRank | References | Authors |
0.40 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haixiang Guo | 1 | 71 | 10.66 |
| Kejun Zhu | 2 | 177 | 22.96 |
| Siwei Gao | 3 | 12 | 1.94 |
| Ting Liu | 4 | 2 | 0.40 |