WeDIV – An improved k-means clustering algorithm with a weighted distance and a novel internal validation index
Designing appropriate similarity metrics (distance) and estimating the optimal number of clusters have been two important issues in cluster analysis. This study proposed an improved k-means clustering algorithm involving a Weighted Distance and a novel Internal Validation index (WeDIV). The weighted...
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| Vydáno v: | Egyptian informatics journal Ročník 23; číslo 4; s. 133 - 144 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.12.2022
Elsevier |
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| ISSN: | 1110-8665, 2090-4754 |
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| Abstract | Designing appropriate similarity metrics (distance) and estimating the optimal number of clusters have been two important issues in cluster analysis. This study proposed an improved k-means clustering algorithm involving a Weighted Distance and a novel Internal Validation index (WeDIV). The weighted distance, EP_dis, was designed by considering the relative contribution between Euclidean and Pearson distances with a weighted strategy. This strategy can effectively capture information reflecting the globally spatial correlation and locally variable trend simultaneously in high-dimensional space. The new internal validation index,RCH, inspired by the Calinski-Harabasz (CH) index and the analysis of variance, was developed to automatically estimate the optimal number of clusters. The EP_dis was proved reliable in mathematics and was validated on two simulated datasets. Four simulated datasets representing different properties were used to validate the effectiveness of RCH. Furthermore, We compared the clustering performance of WeDIV with 12 prevailing clustering algorithms on 16 UCI datasets. The results demonstrated that WeDIV outperforms the others regardless of specifying the number of clusters or not. |
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| AbstractList | Designing appropriate similarity metrics (distance) and estimating the optimal number of clusters have been two important issues in cluster analysis. This study proposed an improved k-means clustering algorithm involving a Weighted Distance and a novel Internal Validation index (WeDIV). The weighted distance, EP_dis, was designed by considering the relative contribution between Euclidean and Pearson distances with a weighted strategy. This strategy can effectively capture information reflecting the globally spatial correlation and locally variable trend simultaneously in high-dimensional space. The new internal validation index,RCH, inspired by the Calinski-Harabasz (CH) index and the analysis of variance, was developed to automatically estimate the optimal number of clusters. The EP_dis was proved reliable in mathematics and was validated on two simulated datasets. Four simulated datasets representing different properties were used to validate the effectiveness of RCH. Furthermore, We compared the clustering performance of WeDIV with 12 prevailing clustering algorithms on 16 UCI datasets. The results demonstrated that WeDIV outperforms the others regardless of specifying the number of clusters or not. |
| Author | Sabo, Umar Jlbrilla Ning, Zilan Chen, Jin Huang, Jianjun Yuan, Zheming Dai, Zhijun |
| Author_xml | – sequence: 1 givenname: Zilan surname: Ning fullname: Ning, Zilan organization: Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha 410128, China – sequence: 2 givenname: Jin surname: Chen fullname: Chen, Jin organization: Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha 410128, China – sequence: 3 givenname: Jianjun surname: Huang fullname: Huang, Jianjun organization: Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha 410128, China – sequence: 4 givenname: Umar Jlbrilla surname: Sabo fullname: Sabo, Umar Jlbrilla organization: Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha 410128, China – sequence: 5 givenname: Zheming surname: Yuan fullname: Yuan, Zheming organization: Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha 410128, China – sequence: 6 givenname: Zhijun surname: Dai fullname: Dai, Zhijun email: daizhijun@hunau.edu.cn organization: Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha 410128, China |
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| Keywords | EP_dis Internal validation index Weighted distance Clustering RCH |
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