Using an Extended Self-Organizing Map Network to Forecast Market Segment Membership

2011 ◽  
pp. 142-157
Author(s):  
Melody Y. Kiang ◽  
Dorothy M. Fisher ◽  
Michael Y. Hu ◽  
Robert T. Chi
Keyword(s):  
Input Data ◽  
Self Organizing Map ◽  
Market Segment ◽  
Extended Version ◽  
Extended Self ◽  
Data Set ◽  
Step Procedure ◽  
Som Network ◽  
Residential Market ◽  
Self Organizing

This chapter presents an extended Self-Organizing Map (SOM) network and demonstrates how it can be used to forecast market segment membership. The Kohonen’s SOM network is an unsupervised learning neural network that maps n-dimensional input data to a lower dimensional (usually one- or two-dimensional) output map while maintaining the original topological relations. We apply an extended version of SOM networks that further groups the nodes on the output map into a user-specified number of clusters to a residential market data set from AT&T. Specifically, the extended SOM is used to group survey respondents using their attitudes towards modes of communication. We then compare the extended SOM network solutions with a two-step procedure that uses the factor scores from factor analysis as inputs to K-means cluster analysis. Results using AT&T data indicate that the extended SOM network performs better than the two-step procedure.

Analysing the Residential Market in Trójmiasto Using Self-Organizing Map

2021 ◽  
Author(s):  
Olgun Aydin ◽  
Krystian Zielinski
Keyword(s):  
Multidimensional Data ◽  
Residential Property ◽  
Self Organizing Map ◽  
Property Market ◽  
Residential Properties ◽  
Price Trends ◽  
Residential Market ◽  
Key Aspects ◽  
The Impact ◽  
Self Organizing

Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

scholarly journals A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

2011 ◽  
pp. 24-32 ◽  
Author(s):  
Nicoleta Rogovschi ◽  
Mustapha Lebbah ◽  
Younès Bennani
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Categorical Variables ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Public Data ◽  
Self Organizing

Most traditional clustering algorithms are limited to handle data sets that contain either continuous or categorical variables. However data sets with mixed types of variables are commonly used in data mining field. In this paper we introduce a weighted self-organizing map for clustering, analysis and visualization mixed data (continuous/binary). The learning of weights and prototypes is done in a simultaneous manner assuring an optimized data clustering. More variables has a high weight, more the clustering algorithm will take into account the informations transmitted by these variables. The learning of these topological maps is combined with a weighting process of different variables by computing weights which influence the quality of clustering. We illustrate the power of this method with data sets taken from a public data set repository: a handwritten digit data set, Zoo data set and other three mixed data sets. The results show a good quality of the topological ordering and homogenous clustering.

New Hardware Architecture for Self-Organizing Map Used for Color Vector Quantization

2019 ◽  
Vol 29 (01) ◽  
pp. 2050002
Author(s):  
Khaled Ben Khalifa ◽  
Ahmed Ghazi Blaiech ◽  
Mehdi Abadi ◽  
Mohamed Hedi Bedoui
Keyword(s):  
Vector Quantization ◽  
Recent Literature ◽  
Network Architectures ◽  
Self Organizing Map ◽  
Description Language ◽  
Field Programmable ◽  
Color Vector ◽  
Hardware Description ◽  
Som Network ◽  
Self Organizing

In this paper, we present a new generic architectural approach of a Self-Organizing Map (SOM). The proposed architecture, called the Diagonal-SOM (D-SOM), is described as an Hardware–Description-Language as an intellectual property kernel with easily adjustable parameters.The D-SOM architecture is based on a generic formalism that exploits two levels of the nested parallelism of neurons and connections. This solution is therefore considered as a system based on the cooperation of a distributed set of independent computations. The organization and structure of these calculations process an oriented data flow in order to find a better treatment distribution between different neuroprocessors. To validate the D-SOM architecture, we evaluate the performance of several SOM network architectures after their integration on a Xilinx Virtex-7 Field Programmable Gate Array support. The proposed solution allows the easy adaptation of learning to a large number of SOM topologies without any considerable design effort. [Formula: see text] SOM hardware is validated through FPGA implementation, where temporal performance is almost twice as fast as that obtained in the recent literature. The suggested D-SOM architecture is also validated through simulation on variable-sized SOM networks applied to color vector quantization.

CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES

2009 ◽  
Vol 18 (08) ◽  
pp. 1353-1367 ◽  
Author(s):  
DONG-CHUL PARK
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Objective Function ◽  
Lagrange Multiplier ◽  
Synthetic Data ◽  
Feature Weighting ◽  
The Self ◽  
Self Organizing Map ◽  
Data Set ◽  
Self Organizing

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy.

A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

2008 ◽  
Vol 07 (04) ◽  
pp. 363-383 ◽  
Author(s):  
MUSTAPHA LEBBAH ◽  
YOUNÈS BENNANI ◽  
NICOLETA ROGOVSCHI
Keyword(s):  
Binary Data ◽  
Learning Algorithm ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Binary Coding ◽  
Public Data ◽  
Multivariate Binary Data ◽  
Self Organizing

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

scholarly journals Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map

2014 ◽  
Vol 8 (1) ◽  
pp. 101
Author(s):  
Ambarwati Ambarwati ◽  
Edi Winarko
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
System Application ◽  
Input Data ◽  
Information Resources ◽  
The Self ◽  
News Article ◽  
Self Organizing Map ◽  
Map Algorithm ◽  
Self Organizing

AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing Map. Artikel berita digunakan sebagai input data. Kemudian sistem melakukan pemrosesan data untuk dikelompokkan. Proses yang dilakukan sistem meliputi preprocessing, feature extraction, clustering dan visualize.Sistem yang dikembangkan mampu menampilkan hasil clustering dengan algoritma Self Organizing Map dan memberikan visualisasi dengan smoothed data histograms berupa island map dari artikel berita. Selain itu sistem dapat menampilkan koleksi dokumen dari lima kategori berita yang ada pada tiap tahunnya dan banyaknya kata (histogram kata) yang sering muncul pada tiap arikel berita. Pengujian dari sistem ini dengan memasukan artikel berita, kemudian sistem memprosesnya dan mampu memberikan hasil cluster dari artikel berita yang dimasukan. Kata kunci—Pengelompokkan berita Indonesia, pengelompokkan berdasar histogram kata, pengelompokan berita menggunakan SOM  Abstract News is awaited information resources by humans every day. Human reading the news with the desired category. If the computer able to news clustering with automatically, humans of course will be easier to read the news according to the desired category. News clustering in the form of news articles with automatically very interesting because it organizes news articles manually takes time and costs not a little bit.The purpose of this research is to create a system application for grouping news articles by using the Self Organizing Map algorithm. News article be used as input into the system. News articles used as input data. Then the system performs data processing until to be clustered. Processes performed by the system covers: preprocessing, feature extraction, clustering and visualize.The system developed is able to display the results clustering of the Self Organizing Map algorithm and gives visualization of the Smoothed Data Histograms in the form of island map from news articles. Additionally the system can display a word histogram and news articles from five categories news in each year. Testing of this system by entering the news articles, then the system performs data processing and gives results of a cluster from news articles that input. Keywords—Indonesia news clustering, clustering based on words histograms, news clustering using SOM

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