Name
Abstract | ||
|---|---|---|
For high dimensional data, the redundant attributes of samplers will not only increase the complexity of the calculation, but also affect the accuracy of final result. The existing attribute reduction methods are encountering bottleneck problem of timeliness and spatiality. In order to looking for a relatively coarse attributes granularity of problem solving, this paper proposes an efficient attribute granulation method to remove redundancy attribute. The method calculates the similarity of attributes according attribute discernibility first, and then clusters attributes into several group through affinity propagation clustering algorithm. At last, representative attributes are produced through some algorithms to form a coarser attribute granularity. Experimental results show that the attribute granulation method based on affinity propagation clustering algorithm(AGAP) method is a more efficient algorithm than traditional attribute reduction algorithm(AR). © 2013 ACADEMY PUBLISHER. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.4304/jsw.8.4.834-841 | JSW |
Keywords | Field | DocType |
ap clustering,attribute dependability,attribute granulation,parallel computing | Data mining,Bottleneck,Clustering high-dimensional data,Computer science,Algorithm,Redundancy (engineering),Artificial intelligence,Granularity,Granulation,Affinity propagation clustering,Machine learning,Attribute domain | Journal |
Volume | Issue | Citations |
8 | 4 | 2 |
PageRank | References | Authors |
0.38 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Zhu | 1 | 81 | 7.20 |
| Shifei Ding | 2 | 1074 | 94.63 |
| Han Zhao | 3 | 12 | 3.99 |
| Lina Bao | 4 | 13 | 1.30 |