PENGARUH ALGORITMA PCA TERHADAP PERHITUNGAN NILAI RENTANG PADA CLUSTERING SELF ORGANIZING MAP

ALBAR, MUHAMMAD and Rini, Dian Palupi and Miraswan, Kanda Januar (2020) PENGARUH ALGORITMA PCA TERHADAP PERHITUNGAN NILAI RENTANG PADA CLUSTERING SELF ORGANIZING MAP. Undergraduate thesis, Sriwijaya University.

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Abstract

Data mining is a pattern recognition process that aims to find important information in data. Clustering is a data mining method. Clustering works by grouping data based on similarities or dissimilarities. Distance measurement is a very important component in clustering. Different distance measurement will produce different clustering values, but before doing a clustering, the data must go through initial processing or what is called preprocessing. Data preprocessing can improve the results and efficiency of a clustering algorithm. Data reduction is a method in data preprocessing that can be applied before carrying out the clustering process. This study will examine the effect of the data reduction algorithm, Principal Component Analysis, on the results of the Self Organizing Map clustering algorithm which uses 3 distance measurement methods, Chebyshev distance, Minkowski distance, and Cosine distance. The clustering results will be evaluated using the Davies Bouldin Index. The results showed that the Chebyshev distance gave the best clustering results and the application of the Principal Component Analysis algorithm had a negative impact on the results of the Self Organizing Map clustering and the 3 distance measurements used.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Data Mining, Clustering, Chebyshev distance, Minkowski distance, Cosine distance, Principal Component Analysis, Self Organizing Map, Davies Bouldin Index
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Muhammad Albar
Date Deposited: 25 Jan 2021 03:15
Last Modified: 25 Jan 2021 03:15
URI: http://repository.unsri.ac.id/id/eprint/40835

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