A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures
Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/10/2226 |
| _version_ | 1798039767808475136 |
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| author | Shaoyi Liang Deqiang Han |
| author_facet | Shaoyi Liang Deqiang Han |
| author_sort | Shaoyi Liang |
| collection | DOAJ |
| description | Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC). Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency. |
| first_indexed | 2024-04-11T21:58:18Z |
| format | Article |
| id | doaj.art-1b6db32f16e544e286baf2a03e86460a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:58:18Z |
| publishDate | 2017-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1b6db32f16e544e286baf2a03e86460a2022-12-22T04:01:02ZengMDPI AGSensors1424-82202017-09-011710222610.3390/s17102226s17102226A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness MeasuresShaoyi Liang0Deqiang Han1MOE KLINNS Lab, Institute of Integrated Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaMOE KLINNS Lab, Institute of Integrated Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaCloseness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distance alone. These metrics quantify the closeness only based on the concerned data points’ positions in the feature space, and they might cause problems when dealing with clustering tasks having arbitrary clusters shapes and different clusters densities. In this paper, we first propose a novel Closeness Measure between data points based on the Neighborhood Chain (CMNC). Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Furthermore, based on CMNC, we also propose a clustering ensemble framework that combines CMNC and geometric-distance-based closeness measures together in order to utilize both of their advantages. In this framework, the “bad data points” that are hard to cluster correctly are identified; then different closeness measures are applied to different types of data points to get the unified clustering results. With the fusion of different closeness measures, the framework can get not only better clustering results in complicated clustering tasks, but also higher efficiency.https://www.mdpi.com/1424-8220/17/10/2226clusteringclustering ensemblecloseness measuregeometric distanceneighborhood chain |
| spellingShingle | Shaoyi Liang Deqiang Han A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures Sensors clustering clustering ensemble closeness measure geometric distance neighborhood chain |
| title | A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures |
| title_full | A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures |
| title_fullStr | A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures |
| title_full_unstemmed | A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures |
| title_short | A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures |
| title_sort | clustering oriented closeness measure based on neighborhood chain and its application in the clustering ensemble framework based on the fusion of different closeness measures |
| topic | clustering clustering ensemble closeness measure geometric distance neighborhood chain |
| url | https://www.mdpi.com/1424-8220/17/10/2226 |
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