Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters

One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive t...

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Tác giả chính:	Khan, Imran
Đồng tác giả:	Luo, Zongwei
Định dạng:	BB
Ngôn ngữ:	English
Thông tin xuất bản:	IEEE Xplore 2020
Chủ đề:	Fuzzy k-means clustering number of clusters data mining variable weighting
Truy cập trực tuyến:	http://tailieuso.tlu.edu.vn/handle/DHTL/9942
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spelling	oai:localhost:DHTL-99422020-12-18T09:01:13Z Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters Khan, Imran Luo, Zongwei Huang, Joshua Zhexue Shahzad, Waseem Fuzzy k-means clustering number of clusters data mining variable weighting One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive to the initialization of cluster centers and utilize variable weights to recover the number of clusters. To fill this gap, we extend the standard fuzzy k-means clustering algorithm. It can automatically determine the number of clusters by iteratively calculating the weights of all variables and the membership value of each object in all clusters. Two new steps are added to the fuzzy k-means clustering process. One of them is to introduce a penalty term to make the clustering process insensitive to the initial cluster centers.The other one is to utilize a formula for iterative updating of variable weights in each cluster based on the current partition of data. Experimental results on real-world and synthetic datasets have shown that the proposed algorithm effectively determined the correct number of clusters while initializing the different number of cluster centroids. We also tested the proposed algorithm on gene data to determine a subset of important genes. https://doi.org/10.1109/TKDE.2019.2911582 2020-12-18T08:59:59Z 2020-12-18T08:59:59Z 2019 BB http://tailieuso.tlu.edu.vn/handle/DHTL/9942 en IEEE Transactions on Knowledge and Data Engineering, (2019), pp 16 application/pdf IEEE Xplore
institution	Trường Đại học Thủy Lợi
collection	DSpace
language	English
topic	Fuzzy k-means clustering number of clusters data mining variable weighting
spellingShingle	Fuzzy k-means clustering number of clusters data mining variable weighting Khan, Imran Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters
description	One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive to the initialization of cluster centers and utilize variable weights to recover the number of clusters. To fill this gap, we extend the standard fuzzy k-means clustering algorithm. It can automatically determine the number of clusters by iteratively calculating the weights of all variables and the membership value of each object in all clusters. Two new steps are added to the fuzzy k-means clustering process. One of them is to introduce a penalty term to make the clustering process insensitive to the initial cluster centers.The other one is to utilize a formula for iterative updating of variable weights in each cluster based on the current partition of data. Experimental results on real-world and synthetic datasets have shown that the proposed algorithm effectively determined the correct number of clusters while initializing the different number of cluster centroids. We also tested the proposed algorithm on gene data to determine a subset of important genes.
author2	Luo, Zongwei
author_facet	Luo, Zongwei Khan, Imran
format	BB
author	Khan, Imran
author_sort	Khan, Imran
title	Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters
title_short	Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters
title_full	Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters
title_fullStr	Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters
title_full_unstemmed	Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters
title_sort	variable weighting in fuzzy k-means clustering to determine the number of clusters
publisher	IEEE Xplore
publishDate	2020
url	http://tailieuso.tlu.edu.vn/handle/DHTL/9942
work_keys_str_mv	AT khanimran variableweightinginfuzzykmeansclusteringtodeterminethenumberofclusters
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Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters

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