Name
Abstract | ||
|---|---|---|
The algorithms of data mining need better efficiency as data scale becomes larger and larger and the dimension of data is more. Aiming at the lower efficiency of the former MST (minimum spanning tree) clustering algorithm based on gene expression, a modified IMST (improved minimum spanning tree) clustering algorithm applied to common problem is brought forward. The analyzing of theory and example show that new IMST clustering algorithm can enhance the efficiency of constructing spanning tree and can solve sorting problem for shorter edges of clustering in minimum spanning trees. At last, both the efficiency of clustering and its effect are improved whole. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FSKD.2007.7 | FSKD (3) |
Keywords | Field | DocType |
data mining,data scale,better efficiency,modified imst,common problem,new imst clustering algorithm,improved minimum,lower efficiency,clustering algorithm,improved minimum spanning tree,example show,minimum spanning tree,gene expression,genetic engineering,spanning tree | CURE data clustering algorithm,Distributed minimum spanning tree,Correlation clustering,Computer science,Artificial intelligence,Spanning tree,Cluster analysis,Kruskal's algorithm,Machine learning,Minimum spanning tree,Single-linkage clustering | Conference |
ISBN | Citations | PageRank |
0-7695-2874-0 | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Xie | 1 | 67 | 7.20 |
| Yu Liang | 2 | 21 | 12.01 |
| Jing Yang | 3 | 0 | 0.34 |