Cited By
View all- Khazaei AKhaleghzadeh HGhasemzadeh M(2021)FOCT: Fast Overlapping Clustering for Textual DataIEEE Access10.1109/ACCESS.2021.31300949(157670-157680)Online publication date: 2021
Overlapping Clustering for Textual Data
Pages 115 - 118
Abstract
Texts have inherent overlapping, therefore for clustering textual data, the overlapping clustering algorithms are more appropriate. In this regard, a major challenge is that they are very slow in clustering big volumes of textual data. Among others, OKM and OSOM are two important overlapping clustering algorithms. In this study, we have implemented and compared the performance of these two algorithms. The experimental results of our study show that OKM clusters have better overlap sizes when these algorithms are used for clustering textual data. Since both of them require much time to complete, none of these two algorithms is suitable for clustering textual data. Therefore we mastermind a fast overlapping version of SOM which is suitable for this purpose.
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Index Terms
- Overlapping Clustering for Textual Data
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Published In
February 2018
349 pages
ISBN:9781450354141
DOI:10.1145/3185089
Copyright © 2018 ACM.
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Published: 08 February 2018
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ICSCA 2018
ICSCA 2018: 2018 7th International Conference on Software and Computer Applications
February 8 - 10, 2018
Kuantan, Malaysia
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View all- Khazaei AKhaleghzadeh HGhasemzadeh M(2021)FOCT: Fast Overlapping Clustering for Textual DataIEEE Access10.1109/ACCESS.2021.31300949(157670-157680)Online publication date: 2021
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