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Cluster analysis in industrial market segmentation through artificial neural network

R.J Kuo, L.M Ho, C.M Hu
2002 Computers & industrial engineering  
The proposed twostage method is the combination of self-organizing feature maps and K-means method.  ...  Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data.  ...  Acknowledgements The authors would like to thank the National Science Council, Taiwan, Republic of China, for partially supporting this manuscript under Contract No. NSC 88-2416-H-027-001.  ... 
doi:10.1016/s0360-8352(02)00048-7 fatcat:rr2ql5mptbbkxerrg24qkd5f3q

Using Clustering Method to Understand Indian Stock Market Volatility

Tamal Datta, Indranil Ghosh
2015 Communications on Applied Electronics  
We use three clustering algorithms namely Kernel K-Means, Self Organizing Maps and Mixture of Gaussian models and two internal clustering validity measures, Silhouette Index and Dunn Index, to assess the  ...  The exercise has been performed for the Indian stock market on daily data for two years. For our analysis we map number of clusters against number of variables.  ...  ) and synaptic adaptation phase (self-organized formation of feature maps).  ... 
doi:10.5120/cae2015651793 fatcat:2fzlxovs2bbuzmmo5nflj7uzti

Application of neural network in market segmentation: A review on recent trends

Manojit Chattopadhyay, Pranab K Dan, Sitanath Majumdar, Partha Sarathi Chakraborty
2012 Management Science Letters  
Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation  ...  Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification  ...  The Kohonan Self-Organizing Map of unsupervised ANN used in clustering for large and complex data.  ... 
doi:10.5267/j.msl.2012.01.013 fatcat:osydpq4ebfauffk27isom3r6gm

Data mining techniques for herbs

J Satish Babu, M Niveditha, V Bhavya, K Gowthami
2017 International Journal of Engineering & Technology  
The main objective of this project is to survey on various data mining methods and their techniques and to conclude the suitable algorithm.  ...  SOM (self-organizing map) is most popular Neural Network provides a topology preserving mapping from the high dimensional space to map units.  ...  using SOM algorithm.The presentation of component plane is of integrated self-organized map is a powerful AI tool for analysis of big, complex, biological databases.  ... 
doi:10.14419/ijet.v7i1.1.9943 fatcat:342syjq7afa4tclqzt6grugioy

Application based, advantageous k-means algorithm

2016 International Journal of Latest Trends in Engineering and Technology  
Different levels of analysis are available like genetic algorithms, artificial neural networks, decision trees, rule based induction methods and data visualization.  ...  K-means clustering has been integrated with these analytical tools as per the requirement of the application area.  ...  We are looking forward to take this review paper by bringing to notice the loop holes of K-means clustering algorithms and suggesting improvements in K-means clustering and other clustering algorithms.  ... 
doi:10.21172/1.72.520 fatcat:5gx7hw7akje3db6xott4tlzxue

A data mining method for service marketing: A case study of banking industry

Mohammad Reza Taghva, Seyed Mojtaba Hosseini Bamakan, Samrand Toufani
2011 Management Science Letters  
Moreover, self-organizing neural network map is used to identify groups of customers based on repayment behavior, recency, frequency, and monetary behavioral scoring predicators.  ...  We also perform more analysis using Apriori association rule to make marketing strategies for services used by banks.  ...  Therefore, clusters are the results of market segmentation and clustering is commonly used for market segmentation.  ... 
doi:10.5267/j.msl.2010.04.004 fatcat:lae7if476feqhd5usjzczl4flu

Page 987 of The Journal of the Operational Research Society Vol. 55, Issue 9 [page]

2004 The Journal of the Operational Research Society  
Integration of self- organizing feature map and K-means algorithm for market segmentation. Comput Oper Res 29: 1475-1493. Clausi (2002).  ...  Comparing performance of feed-forward neural nets and K-means for cluster-based market segmentation. Eur J Opl Res 114(2): 346-353. Lee H-S (1999).  ... 

Customer Segmentation Based on Self-Organizing Maps: A Case Study on Airline Passengers

İlkay Yelmen, Serpil Üstebay, Metin Zontul
2020 Journal of Aeronautics and Space Technologies (Havacilik ve Uzay Teknolojileri Dergisi)  
clustered by using self-organizing map method and totally 15 clusters were obtained.In purchasing trends, the highest return was obtained in cluster 2 and the minimum return in cluster 6.  ...  Customer segmentation is acustomer grouping modelbased on common featuresand it directly relates with customer satisfaction of the companies.It provides access to the right customer with the right methods  ...  CLUSTERING Self-organizing maps (SOM), is one of the unsupervised learning methods developed by Kohonen [26] .  ... 
doaj:500a4a2ca3e34b5391492e886b5c7041 fatcat:byxhbvif6bcobjyovofvmzom5a

Difference Analysis of Regional Economic Development Based on the SOM Neural Network with the Hybrid Genetic Algorithm

Ying Cai, Xu Wang, LiRan Xiong, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
In this paper, a self-organizing feature map (SOM) neural network with the hybrid genetic algorithm is used to cluster the differences of regional economic development, the clustering results are evaluated  ...  Clustering analysis is a data mining method that clusters or classifies entities according to their characteristics and then discovers the whole spatial distribution law of datasets and typical patterns  ...  Compared with other clustering methods, self-organizing feature map (SOM) neural network can realize real-time learning and has self-stability [14] .  ... 
doi:10.1155/2021/6734345 pmid:34512744 pmcid:PMC8429017 fatcat:j72m3ntgq5hkhpgu6gpsotscli

Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification

Udoinyang G. Inyang, Okure O. Obot, Moses E. Ekpenyong, Aliu M. Bolanle
2017 Modern Applied Science  
This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis.  ...  K-means algorithm was used to partition the entire input space of customers' transaction dataset into 3 and 4 disjoint segments based on customers' frequency (F) and monetary value (MV).  ...  We adopt hybridized unsupervised learning tools -k-means algorithm and self-organizing maps (SOM), in the development of a CRM framework for discovery and visualization of underlying clusters and customers  ... 
doi:10.5539/mas.v11n9p151 fatcat:2j5uonahs5eelnilqluynugn2i

Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods

Kit Yan Chan, C.K. Kwong, B.Q. Hu
2012 Applied Soft Computing  
The methodology is an integration of a fuzzy compression technique for multi-dimension reduction and a fuzzy clustering technique.  ...  After the fuzzy data is clustered into marketing segments, the centre points of market segments are used as ideal points for new product development.  ...  Self-organized feature map is widely used for dimension reduction and clustering, concurrently for various applications of which the data is in multi-dimensions [9] [10] [11] .  ... 
doi:10.1016/j.asoc.2011.11.026 fatcat:o2tcuh36cvc63nzu4lwcsuaysa

Data Analysis and Bioinformatics [chapter]

Vito Di Gesù
2007 Lecture Notes in Computer Science  
Data analysis methods and techniques are revisited in the case of biological data sets. Particular emphasis is given to clustering and mining issues.  ...  Clustering is still a subject of active research in several fields such as statistics, pattern recognition, and machine learning.  ...  of the technique devised in [53] for clustering based on Genetic Algorithms.  ... 
doi:10.1007/978-3-540-77046-6_47 fatcat:piggpbyzmvclvd5bjduv4nov4m

Application of data mining techniques in customer relationship management: A literature review and classification

E.W.T. Ngai, Li Xiu, D.C.K. Chau
2009 Expert systems with applications  
Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM.  ...  , Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization).  ...  .: G-YF20) and National Natural Science Foundation of China (NSFC, Project no.: 70671059).  ... 
doi:10.1016/j.eswa.2008.02.021 fatcat:nqlw5dkqjzfhpf2cldlmoxd25e

Data Mining Application for Finding Patterns: Survey of Large Data Research Tools

Aive Islam
2017 American Journal of Neural Networks and Applications  
Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques.  ...  In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed.  ...  It was developed self organizing maps as a way of automatically detecting strong features in large data sets.  ... 
doi:10.11648/j.ajnna.20170302.11 fatcat:mjqek24re5faxbxfax4xgtwebq

A Review on Clustering Technique

Vivek Kumar
2015 International Journal on Recent and Innovation Trends in Computing and Communication  
Example of Competitive learning, SOM and ART are famous for clustering. SOM have the limitation of dimension, ART is good but computation cost is very high.  ...  Artificial Neural Network is very powerful tool in machine learning or in the field of computer visions. Competitive learning is used for Clustering in Neural network.  ...  Various clustering methods have been developed, including hierarchical approaches such as complete-link algorithms, partitional approaches , and Self-Organizing Maps.  ... 
doi:10.17762/ijritcc2321-8169.1503136 fatcat:rdt7mpmyvnbm3ph6o7v72wmpdy
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