Name
Abstract | ||
|---|---|---|
Efficient partitioning of large data sets into homogeneous clusters is fundamental problem in data mining. The hierarchical clustering methods are not adaptable because of their high computational complexity. The K-means based algorithms give promising results for their efficiency. However their use in often limited to numeric data. The quality of clusters produced depends on the initialization of clusters and the order in which is based on the K-means philosophy but removes the numeric data limitation. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/3-540-48298-9_22 | DaWaK |
Keywords | Field | DocType |
data mining,large data set,k-means clustering algorithm,homogeneous cluster,numeric data limitation,hierarchical clustering method,efficient partitioning,fundamental problem,k-means philosophy,categorical attributes,high computational complexity,hierarchical clustering,k means clustering,k means,computational complexity | Hierarchical clustering,k-means clustering,Fuzzy clustering,Data mining,CURE data clustering algorithm,Affinity propagation,Correlation clustering,Computer science,Determining the number of clusters in a data set,Single-linkage clustering | Conference |
ISBN | Citations | PageRank |
3-540-66458-0 | 19 | 1.24 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. K. Gupta | 1 | 274 | 42.67 |
| K. Sambasiva Rao | 2 | 19 | 1.24 |
| Vasudha Bhatnagar | 3 | 181 | 17.69 |