Assessment of Clusteranalysis and Self-Organizing Maps

Market segmentation represents a central problem of preparing marketing activities. The methodical approach of this problem is supported by clustering methods. Available data are used to detect common grounds regarding their quality structures. Therefore statistics provides various methods for cluster analysis. Self-organizing maps are another possibility to form classes. They are a special approach of the artificial neural networks. The statistical methods and these methods, which are based on organic processes of our brain, offer different solutions although the starting conditions are the same. Often decisions about investigations are based on such solutions. Therefore the results of clustering are very important to reveal systematic information about the size of classes and their structure. Methodical notes are needed for the use of any clustering method. This paper offers a simplified way to select the best result for clustering.

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Business Client Segmentation in Banking Using Self-Organizing Maps

South East European Journal of Economics and Business ◽

10.2478/jeb-2013-0007 ◽

2014 ◽

Vol 8 (2) ◽

pp. 32-41 ◽

Cited By ~ 6

Author(s):

Mirjana Pejić Bach ◽

Sandro Juković ◽

Ksenija Dumičić ◽

Nataša Šarlija

Keyword(s):

Foreign Trade ◽

Selection Criteria ◽

Clustering Algorithm ◽

Annual Income ◽

Clustering Methods ◽

Self Organizing Maps ◽

Marketing Activities ◽

Ward Clustering ◽

Statistical Clustering ◽

Self Organizing

Abstract Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means). The goal of the paper is to demonstrate that self-organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

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Spam Review Detection Using Self-organizing Maps and Convolutional Neural Networks

Computers & Security ◽

10.1016/j.cose.2021.102274 ◽

2021 ◽

pp. 102274

Author(s):

Ashraf Neisari ◽

Luis Rueda ◽

Sherif Saad

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Self Organizing Maps ◽

Self Organizing

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Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Sustainability ◽

10.3390/su13158295 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8295

Author(s):

Patricia Melin ◽

Oscar Castillo

Keyword(s):

Neural Networks ◽

Time Series ◽

Temporal Trends ◽

Main Idea ◽

Temporal Analysis ◽

Virus Propagation ◽

Self Organizing Maps ◽

Fractal Approach ◽

The Times ◽

Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

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Analysis of students’ behaviour through user clustering in online learning settings, based on Self Organizing Maps neural networks

IEEE Access ◽

10.1109/access.2021.3115024 ◽

2021 ◽

pp. 1-1

Author(s):

Soledad Delgado ◽

Federico Moran ◽

Jose Carlos San Jose ◽

Daniel Burgos

Keyword(s):

Neural Networks ◽

Online Learning ◽

Self Organizing Maps ◽

User Clustering ◽

Self Organizing

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Using Self Organizing Maps for Banking Oversight

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch049 ◽

2016 ◽

pp. 1306-1332 ◽

Cited By ~ 1

Author(s):

Felix Lopez-Iturriaga ◽

Iván Pastor-Sanz

Keyword(s):

Neural Networks ◽

Financial Stability ◽

Bankruptcy Prediction ◽

Self Organizing Maps ◽

Policy Makers ◽

Savings Banks ◽

Visual Tool ◽

The Government ◽

Over Time ◽

Self Organizing

This chapter combines two methods based on neural networks (trait recognition and self-organizing maps) to develop a model of bankruptcy prediction. The authors apply the method to the Spanish savings banks, most of them rescued by the Government between 2008 and 2013 in a costly massive process. First, the authors detect the combinations of variables (performance, asset structure, and capitalization) that best describe the profile of the rescued savings banks. Then, the authors use these combinations on a yearly basis to generate bi-dimensional maps in which banks are placed according to their risk and similarities. This method provides a visual tool that can improve the oversight of policy makers on the whole financial system and enable time pertinent answers to some threatens to the country financial stability. The maps are useful means to detect and understand how the financial threats emerge over time too.

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Segmentation of the Portuguese Clients of Pousadas de Portugal

Neural Networks in Business ◽

10.4018/978-1-930708-31-0.ch005 ◽

2002 ◽

pp. 70-88 ◽

Cited By ~ 1

Author(s):

Margarida G.M.S. Cardoso ◽

Fernando Moura-Pires

Keyword(s):

Market Segmentation ◽

A Priori ◽

Neural Nets ◽

Self Organizing Maps ◽

Self Organizing

The aim of our work is to perform a market segmentation of the clients of Pousadas de Portugal, a network for over 40 high-end small hotels, ENATUR. The data for this work was provided by a sample of more than 2500 clients that filled in a given questionnaire. The segmentation is based on how often the clients used the hotels, and on the type of stay they were seeking. A few different techniques were used: mixed approaches using a-priori constitution of clusters and/or neural nets (SOM – Self-Organizing Maps) and/or k-means. Profiling the obtained segments adds some new insights about the clients and helps ENATUR managers to better support new marketing decisions.

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