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| Content Provider | IET Digital Library |
|---|---|
| Author | Ma, Jingjing Jiang, Xiangming Gong, Maoguo |
| Abstract | Here, the authors propose a novel two-phase clustering algorithm with a density exploring distance (DED) measure. In the first phase, the fast global K-means clustering algorithm is used to obtain the cluster number and the prototypes. Then, the prototypes of all these clusters and representatives of points belonging to these clusters are regarded as the input data set of the second phase. Afterwards, all the prototypes are clustered according to a DED measure which makes data points locating in the same structure to possess high similarity with each other. In experimental studies, the authors test the proposed algorithm on seven artificial as well as seven UCI data sets. The results demonstrate that the proposed algorithm is flexible to different data distributions and has a stronger ability in clustering data sets with complex non-convex distribution when compared with the comparison algorithms. |
| Starting Page | 59 |
| Ending Page | 64 |
| Page Count | 6 |
| Volume Number | 3 |
| e-ISSN | 24682322 |
| Issue Number | Issue 1, Mar (2018) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/trit/3/1 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/trit.2018.0006 |
| Journal | CAAI Transactions on Intelligence Technology |
| Publisher | The Institution of Engineering and Technology Chongqing University of Technology Chinese Association for Artificial Intelligence |
| Publisher Date | 2018-03-05 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Cluster Number Combinatorial Mathematics Comparison Algorithm Data Distributions Data Handling Technique Data Points DED Measure Density Exploring Distance Measure Fast Global K-means Clustering Algorithm Non-convex Distribution Pattern Clustering Sorting Statistical Distribution Statistics Two-phase Clustering Algorithm UCI Data Sets |
| Content Type | Text |
| Resource Type | Article |
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