The Impact of Exponent Variable on the Performance and Effectiveness of FCM Algorithm for Ontology Construction in Structured Knowledge Management

• Main Authors: Mahmood, Khalid, Rahmah, Mokhtar, Fauziah, Zainuddin, Nor Azhar, Ahmad, Noraziah, Ahmad, Norshita, MN
• Format: Conference or Workshop Item
• Language: English
• Published: IEEE 2021
• Subjects: QA75 Electronic computers. Computer science
• Online Access: 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

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.