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

2020 ◽  
Vol 2 (2) ◽  
pp. 49-54
Author(s):  
Yunita Sinambela ◽  
Sukrina Herman ◽  
Ahsani Takwim ◽  
Septian Rheno Widianto
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Life Time ◽  
Customer Segmentation ◽  
Customer Data ◽  
Fuzzy C Means ◽  
Consumer Segmentation ◽  
Fuzzy C Means Clustering ◽  
Role Based ◽  
And Performance

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.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

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