Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function
Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for...
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| Veröffentlicht in: | International journal of computer mathematics Jg. 94; H. 4; S. 663 - 675 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Abingdon
Taylor & Francis
03.04.2017
Taylor & Francis Ltd |
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| Abstract | Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis. |
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| AbstractList | Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis. |
| Author | Zhang, Tengfei Ma, Fumin |
| Author_xml | – sequence: 1 givenname: Tengfei surname: Zhang fullname: Zhang, Tengfei email: tfzhang@126.com organization: College of Automation, Nanjing University of Posts and Telecommunications – sequence: 2 givenname: Fumin surname: Ma fullname: Ma, Fumin organization: College of Information Engineering, Nanjing University of Finance and Economics |
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| SubjectTerms | Algorithms Approximation Cluster analysis Clustering Clustering algorithm Clusters Computer simulation Gaussian function Mathematical analysis Mathematical models rough k-means rough set theory Routing Vector quantization weighted distance measure |
| Title | Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function |
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