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

  • Yunita Sinambela Magister of Information System STMIK LIKMI, Bandung, Indonesia
  • Sukrina Herman Magister of Information System STMIK LIKMI, Bandung, Indonesia
  • Ahsani Takwim Magister of Information System STMIK LIKMI, Bandung, Indonesia
  • Septian Rheno Widianto Magister of Information System STMIK LIKMI, Bandung, Indonesia
Keywords: Data mining, consumer segmentation, clustering, K-MEANS, FUZZY C MEANS ALGORITHMS

Abstract

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.

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References

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Published
2020-03-15
How to Cite
Sinambela, Y., Herman, S., Takwim, A., & Widianto, S. (2020). 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, 2(2), 49-54. https://doi.org/10.34288/jri.v2i2.116
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