Two-phase clustering algorithm with density exploring distance measure
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 o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-04-01
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| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://digital-library.theiet.org/content/journals/10.1049/trit.2018.0006 |
| _version_ | 1818656791878172672 |
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| author | Jingjing Ma Xiangming Jiang Maoguo Gong |
| author_facet | Jingjing Ma Xiangming Jiang Maoguo Gong |
| author_sort | Jingjing Ma |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T03:31:12Z |
| format | Article |
| id | doaj.art-365a1e9ef6ff4e50b94bd29bd6c51922 |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-12-17T03:31:12Z |
| publishDate | 2018-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-365a1e9ef6ff4e50b94bd29bd6c519222022-12-21T22:05:16ZengWileyCAAI Transactions on Intelligence Technology2468-23222018-04-0110.1049/trit.2018.0006TRIT.2018.0006Two-phase clustering algorithm with density exploring distance measureJingjing Ma0Xiangming Jiang1Maoguo Gong2Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian UniversityKey Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian UniversityKey Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian UniversityHere, 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.https://digital-library.theiet.org/content/journals/10.1049/trit.2018.0006pattern clusteringstatistical distributionssortingDED measuredata pointsUCI data setscomparison algorithmstwo-phase clustering algorithmcluster numberdensity exploring distance measurefast global K-means clustering algorithmdata distributionsnon-convex distribution |
| spellingShingle | Jingjing Ma Xiangming Jiang Maoguo Gong Two-phase clustering algorithm with density exploring distance measure CAAI Transactions on Intelligence Technology pattern clustering statistical distributions sorting DED measure data points UCI data sets comparison algorithms two-phase clustering algorithm cluster number density exploring distance measure fast global K-means clustering algorithm data distributions non-convex distribution |
| title | Two-phase clustering algorithm with density exploring distance measure |
| title_full | Two-phase clustering algorithm with density exploring distance measure |
| title_fullStr | Two-phase clustering algorithm with density exploring distance measure |
| title_full_unstemmed | Two-phase clustering algorithm with density exploring distance measure |
| title_short | Two-phase clustering algorithm with density exploring distance measure |
| title_sort | two phase clustering algorithm with density exploring distance measure |
| topic | pattern clustering statistical distributions sorting DED measure data points UCI data sets comparison algorithms two-phase clustering algorithm cluster number density exploring distance measure fast global K-means clustering algorithm data distributions non-convex distribution |
| url | https://digital-library.theiet.org/content/journals/10.1049/trit.2018.0006 |
| work_keys_str_mv | AT jingjingma twophaseclusteringalgorithmwithdensityexploringdistancemeasure AT xiangmingjiang twophaseclusteringalgorithmwithdensityexploringdistancemeasure AT maoguogong twophaseclusteringalgorithmwithdensityexploringdistancemeasure |