Name
Abstract | ||
|---|---|---|
In this paper, we propose an ensemble clustering method for high dimensional data which uses FastMap projection to generate subspace component data sets. In comparison with popular random sampling and random projection, FastMap projection preserves the clustering structure of the original data in the component data sets so that the performance of ensemble clustering is improved significantly. We present two methods to measure preservation of clustering structure of generated component data sets. The comparison results have shown that FastMap preserved the clustering structure better than random sampling and random projection. Experiments on three real data sets were conducted with three data generation methods and three consensus functions. The results have shown that the ensemble clustering with FastMap projection outperformed the ensemble clusterings with random sampling and random projection. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-13186-3_43 | PAKDD Workshops |
Keywords | Field | DocType |
Ensemble clustering,FastMap,Random sampling,Random projection,Consensus function | Random projection,Data mining,Clustering high-dimensional data,Data set,Pattern recognition,Subspace topology,Computer science,Consensus function,Sampling (statistics),Artificial intelligence,Cluster analysis,Test data generation | Conference |
Volume | ISSN | Citations |
8643 | 0302-9743 | 3 |
PageRank | References | Authors |
0.39 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Imran Khan | 1 | 24 | 2.71 |
| Joshua Zhexue Huang | 2 | 1365 | 82.64 |
| Thanh Tung Nguyen | 3 | 54 | 9.63 |
| Graham J. Williams | 4 | 1041 | 90.42 |