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Cluster analysis in industrial market segmentation through artificial neural network
2002
Computers & industrial engineering
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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
2015
Communications on Applied Electronics
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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
2012
Management Science Letters
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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
2017
International Journal of Engineering & Technology
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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
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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
2011
Management Science Letters
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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
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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
2020
Journal of Aeronautics and Space Technologies (Havacilik ve Uzay Teknolojileri Dergisi)
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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
2021
Computational Intelligence and Neuroscience
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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
2017
Modern Applied Science
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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
2012
Applied Soft Computing
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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]
2007
Lecture Notes in Computer Science
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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
2009
Expert systems with applications
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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
2017
American Journal of Neural Networks and Applications
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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
2015
International Journal on Recent and Innovation Trends in Computing and Communication
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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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