A K-partitioning algorithm for clustering large-scale spatio-textual data

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• 作者：Dong-Wan Choi^a ; ^{dongwanc@sfu.ca} ; Chin-Wan Chung^b ; ^c ; ^{chung_cw@kaist.ac.kr}
• 关键词：Spatio-textual similarity ; K-means clustering ; K-medoids clustering ; K-prototypes clustering ; Expected distance ; Grid partitioning
• 刊名：Information Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：64
• 期：Complete
• 页码：1-11
• 全文大小：1540 K
• 卷排序：64

文摘

The problem of clustering large-scale spatio-textual data is firstly studied. It has many real applications like location-based data cleaning. A modified version of the k-means clustering algorithm is developed for spatio-textual data using the expected pairwise distance. Experimentally, our algorithm is not only fast enough to tackle a massive spatio-textual dataset, but also fairly effective in terms of the quality.