The Impact of Exponent Variable on the Performance and Effectiveness of FCM Algorithm for Ontology Construction in Structured Knowledge Management
Data Clustering is an effective mechanism for the clustering of similar data objects. Fuzzy logic based or soft data clustering technique is very suitable for the formation of data clusters semantically when a data object is the member of more than one clusters unlike hard data clustering technique...
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| Main Authors: | , , , , , |
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| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
IEEE
2021
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| 主題: | |
| 在線閱讀: | http://umpir.ump.edu.my/id/eprint/32076/1/125.pdf http://umpir.ump.edu.my/id/eprint/32076/ https://doi.org/10.1109/ICSECS52883.2021.00128 |
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| 總結: | Data Clustering is an effective mechanism for the
clustering of similar data objects. Fuzzy logic based or soft data clustering technique is very suitable for the formation of data clusters semantically when a data object is the member of more than one clusters unlike hard data clustering techniques. Fuzzy exponent variable is one of the most important variable in fuzzy logic based clustering technique. This variable has significant impact on the performance and effectiveness of the clustering results. An experimental evaluation is performed to analyze the impact of fuzzy exponent variable in this study. The experiments
are performed on textual web document dataset through Fuzzy C-Means soft clustering technique. The results retrieved with various values of fuzzy exponent variable, are analyzed and presented to represent the performance and effectiveness. |
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