PERBANDINGAN TEKNIK REDUKSI DIMENSI ANTARA ALGORITMA PRINCIPAL COMPONENT ANALYSIS DENGAN FUZZY ASSOCIATION RULE TERHADAP HASIL PENGKLASTERAN

LARASWATI, RADEN RORO AYU and Jambak, M. Ihsan and Rodiah, Desty (2020) PERBANDINGAN TEKNIK REDUKSI DIMENSI ANTARA ALGORITMA PRINCIPAL COMPONENT ANALYSIS DENGAN FUZZY ASSOCIATION RULE TERHADAP HASIL PENGKLASTERAN. Undergraduate thesis, Sriwijaya University.

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Abstract

Clustering is the process of grouping data into several groups, where each member of the group has big similarities and disimilarities to other member of the group. In clustering, conventional algorithm works well in handling low-dimensional data, therefore to improve the quality of text clustering results, dimensional reduction technique is required. Dimensional reduction techniques are classified into 2 types, feature selection and feature extraction. This study will compare the application of the Principal Component Analysis (PCA) and Fuzzy Association Rule as a feature extraction technique for k-Means Clustering algorithm. The results obtained by the combination of Fuzzy Association Rule and k-Means improve the performance of text clustering by 22,04%, while combination of PCA and k-Means just improve the performance of text clustering by 18,05%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Dimension Reduction, Principal Component Analysis, Fuzzy Association Rule, k–Means Clustering
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 9286 not found.
Date Deposited:	17 Dec 2020 02:16
Last Modified:	17 Dec 2020 02:16
URI:	http://repository.unsri.ac.id/id/eprint/38720

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