A STUDY OF COMPARING CONCEPTUAL AND PERFORMANCE OF K-MEANS AND FUZZY C MEANS ALGORITHMS (CLUSTERING METHOD OF DATA MINING) OF CONSUMER SEGMENTATION

Jurnal Riset Informatika

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Title A STUDY OF COMPARING CONCEPTUAL AND PERFORMANCE OF K-MEANS AND FUZZY C MEANS ALGORITHMS (CLUSTERING METHOD OF DATA MINING) OF CONSUMER SEGMENTATION
 
Creator Sinambela, Yunita
Herman, Sukrina
Takwim, Ahsani
Widianto, Septian Rheno
 
Description Consumers an important asset in a company that should be maintained properly especially potential customers. Tight competition requires companies to focus on the needs of the customer wants. Consumer segmentation is one of the processes carried out in the marketing strategy. To support the grouping process results consumers or consumer segmentation data mining is the support of a very important role. Based on mapping studies on data mining in support of consumer segmentation obtained two algorithms are often used for consumer segmentation include a K-Means Clustering and Fuzzy C-Means clustering. The attributes used for mining in customer segmentation processes are customer data, products, demographics, consumer behavior, transactions, RFMDC, RFM (Recency, Frequency Monetary) and LTV (Life Time Value). And it is important to combine the clustering algorithm to algorithm Classification, Association, and CPV to get the potential value of each cluster.
 
Publisher Kresnamedia Publisher
 
Date 2020-03-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ejournal.kresnamediapublisher.com/index.php/jri/article/view/116
10.34288/jri.v2i2.116
 
Source Jurnal Riset Informatika; Vol 2 No 2 (2020): Period of March 2020; 49-54
2656-1735
2656-1743
10.34288/jri.v2i2
 
Language eng
 
Relation http://ejournal.kresnamediapublisher.com/index.php/jri/article/view/116/42
 
Rights Copyright (c) 2020 Yunita Sinambela, Sukrina Herman, Ahsani Takwim, Septian Rheno Widianto
http://creativecommons.org/licenses/by-nc/4.0
 

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