Improved multi-objective clustering with automatic determination of the number of clusters

2016 ◽  
Vol 28 (8) ◽  
pp. 2255-2275 ◽  
Author(s):  
María-Guadalupe Martínez-Peñaloza ◽  
Efrén Mezura-Montes ◽  
Nicandro Cruz-Ramírez ◽  
Héctor-Gabriel Acosta-Mesa ◽  
Homero-Vladimir Ríos-Figueroa
Keyword(s):  
Automatic Determination ◽  
Number Of Clusters ◽  
Multi Objective

SEQUENTIAL EXTRACTION OF FUZZY REGRESSION MODELS: LEAST SQUARES AND LEAST ABSOLUTE DEVIATIONS

2011 ◽  
Vol 19 (supp01) ◽  
pp. 53-63 ◽  
Author(s):  
HENGJIN TANG ◽  
SADAAKI MIYAMOTO
Keyword(s):  
Least Squares ◽  
Regression Models ◽  
Fuzzy Regression ◽  
Noise Detection ◽  
Automatic Determination ◽  
Number Of Clusters ◽  
Least Absolute Deviations ◽  
Input And Output ◽  
Scalar Input

Fuzzy c-regression models are known to be useful in real applications, but there are two drawbacks: strong dependency on the predefined number of clusters and sensitiveness against outliers or noises. To avoid these drawbacks, we propose sequential fuzzy regression models based on least absolute deviations which we call SFCRMLAD. This algorithm sequentially extracts one cluster at a time using a method of noise-detection, enabling the automatic determination of clusters and having robustness to noises. We compare this method with the ordinary fuzzy c-regression models based on least squares, fuzzy c-regression models based on least absolute deviations, and moreover sequential fuzzy regression models based on least squares. For this purpose we use a two-dimensional illustrative example whereby characteristics of the four methods are made clear. Moreover a simpler and more efficient algorithm of SFCRMLAD can be used for scalar input and output variables, while a general algorithm of SFCRMLAD uses linear programming solutions for multivariable input. By using the above example, we compare efficiency of different algorithms.

scholarly journals Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering

2020 ◽  
Vol 20 (2) ◽  
pp. 156-167
Author(s):  
Norah Ibrahim Fantoukh ◽  
Mohamed Maher Ben Ismail ◽  
Ouiem Bchir
Keyword(s):  
Fuzzy Clustering ◽  
Automatic Determination ◽  
Number Of Clusters

Application of clustering algorithms to analyze the customer base of the store

2021 ◽  
Vol 14 (2) ◽  
pp. 4-12
Author(s):  
Svetlana Evdokimova ◽  
Aleksandr Zhuravlev ◽  
Tatyana Novikova
Keyword(s):  
Em Algorithm ◽  
Clustering Algorithms ◽  
Spare Parts ◽  
Clustering Methods ◽  
Automatic Determination ◽  
Em Algorithms ◽  
Number Of Clusters ◽  
Customer Base ◽  
Kohonen Networks

This paper analyzes the buyers of the BigCar store, which sells spare parts for trucks, using clustering methods. The algorithms of k-means, g-means, EM and construction of Kohonen networks are considered. For their implementation, the Loginom Community analytical platform is used. Based on sales data for 3 years, buyers are divided into 3 clusters by implementing the k-means, EM algorithms and building a self-organizing Kohonen network. An EM algorithm was also performed with automatic determination of the number of clusters and g-means, which divided buyers into 9 and 10 clusters. The analysis of the resulting clusters showed that the results of the k-means and Kohonen algorithms are better suited to increase sales efficiency.

Sequential Regression Models with Pairwise Constraints Using Noise Clusters

2012 ◽  
Vol 16 (7) ◽  
pp. 814-818 ◽  
Author(s):  
Hengjin Tang ◽  
◽  
Sadaaki Miyamoto
Keyword(s):  
Regression Models ◽  
Switching Regression ◽  
Automatic Determination ◽  
Number Of Clusters ◽  
Pairwise Constraints ◽  
Numerical Examples ◽  
Sequential Regression ◽  
Detecting Method ◽  
Switching Regression Models

Switching regression models are useful in a variety of real applications. Semi-supervised clustering with pairwise constraints is also well-known to be important and many researchers recently study this subject. In spite of their usefulness, there is one drawback: the results have a strong dependency on the predefined number of clusters. To avoid this drawback, we use a method of sequentially extracting one cluster at a time using noise-detecting method, and propose constrained switching regressionmodels which enables an automatic determination of clusters. We show the effectiveness of the proposed method by using numerical examples.

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