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

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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.

Availability
Inventory Code Barcode Call Number Location Status
2007000882T40835T408352020Central Library (REFERENCES)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T408352020
Publisher
Indralaya : Jurusan Teknik Informatika Fakultas Ilmu Komputer Universtas Sriwijaya.,
Collation
xx, 102 hlm.; ill.; 28 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
005.07
Content Type
Text
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Jurusan Teknik Informatika
Algoritma PCA
Specific Detail Info
-
Statement of Responsibility
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No other version available

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