Name
Abstract | ||
|---|---|---|
In this paper, we propose an unsupervised genetic clustering algorithm, which produces a new chromosome without any conventional genetic operators, and instead according to the gene reproducing probabilities determined by Markov chain modeling. Selection of cluster centers from the dataset enables construction of a look-up table that saves the distances between all pairs of data points. The experimental results show that the proposed algorithm not only solves the premature problem to provide a more stable clustering performance in terms of number of clusters and clustering results, but also improves the time efficiency |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICPR.2006.1002 | ICPR (2) |
Keywords | Field | DocType |
unsupervised genetic clustering algorithm,clustering result,pattern clustering,markov chain modeling,winner-population markov chain,look-up table,cluster center,look-up tables,data point,gene reproducing probabilities,proposed algorithm,genetic algorithms,robust clustering,conventional genetic operator,markov processes,table lookup,stable clustering performance,genetics,look up table,markov chain model,look up tables,genetic operator,markov chain | Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Pattern recognition,Correlation clustering,Computer science,Determining the number of clusters in a data set,Nearest-neighbor chain algorithm,Artificial intelligence,Cluster analysis,Single-linkage clustering | Conference |
Volume | ISSN | ISBN |
2 | 1051-4651 | 0-7695-2521-0 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fuwen Yang | 1 | 1051 | 74.00 |
| Hwei-jen Lin | 2 | 59 | 8.91 |
| patrick s p wang | 3 | 303 | 47.66 |
| Hung-Hsuan Wu | 4 | 4 | 1.46 |