Neural networks in data mining

To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Classical methods of gaining knowledge from data sets are statistical methods. In data mining, new methods besides statistical are used. These new methods have their origin in artificial intelligence. They look for unknown and unexpected relations, which can be uncovered by exploring of data in database. In the article, a utilization of modern methods of data mining is described and especially the methods based on neural networks theory are pursued. The advantages and drawbacks of applications of multiplayer feed forward neural networks and Kohonen’s self-organizing maps are discussed. Kohonen’s self-organizing map is the most promising neural data-mining algorithm regarding its capability to visualize high-dimensional data.

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Matching Data Mining Algorithm Suitability to Data Characteristics Using a Self-Organizing Map

Hybrid Information Systems ◽

10.1007/978-3-7908-1782-9_13 ◽

2002 ◽

pp. 169-179 ◽

Cited By ~ 8

Author(s):

Kate A. Smith ◽

Frederick Woo ◽

Vic Ciesielski ◽

Remzi Ibrahim

Keyword(s):

Data Mining ◽

Data Mining Algorithm ◽

Self Organizing Map ◽

Mining Algorithm ◽

Self Organizing

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VISUAL APPROACH TO SUPERVISED VARIABLE SELECTION BY SELF-ORGANIZING MAP

International Journal of Neural Systems ◽

10.1142/s0129065705000098 ◽

2005 ◽

Vol 15 (01n02) ◽

pp. 101-110 ◽

Cited By ~ 1

Author(s):

TIMO SIMILÄ ◽

SAMPSA LAINE

Keyword(s):

Variable Selection ◽

The Self ◽

Data Sets ◽

Self Organizing Map ◽

Robust Method ◽

Relevant Variables ◽

Visual Approach ◽

Predefined Criterion ◽

Target Data ◽

Self Organizing

Practical data analysis often encounters data sets with both relevant and useless variables. Supervised variable selection is the task of selecting the relevant variables based on some predefined criterion. We propose a robust method for this task. The user manually selects a set of target variables and trains a Self-Organizing Map with these data. This sets a criterion to variable selection and is an illustrative description of the user's problem, even for multivariate target data. The user also defines another set of variables that are potentially related to the problem. Our method returns a subset of these variables, which best corresponds to the description provided by the Self-Organizing Map and, thus, agrees with the user's understanding about the problem. The method is conceptually simple and, based on experiments, allows an accessible approach to supervised variable selection.

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A SELF-ORGANIZING MAP FOR MIXED CONTINUOUS AND CATEGORICAL DATA

International Journal of Computing ◽

10.47839/ijc.10.1.733 ◽

2011 ◽

pp. 24-32 ◽

Cited By ~ 1

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.

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Mining Environmental Data in the ADMIRE Project Using New Advanced Methods and Tools

Technology Integration Advancements in Distributed Systems and Computing ◽

10.4018/978-1-4666-0906-8.ch018 ◽

2012 ◽

pp. 296-308

Author(s):

Ondrej Habala ◽

Martin Šeleng ◽

Viet Tran ◽

Branislav Šimo ◽

Ladislav Hluchý

Keyword(s):

Data Mining ◽

Environmental Data ◽

Environmental Applications ◽

Data Sets ◽

Distributed Data ◽

New Methods ◽

Prospective Application ◽

Using Data ◽

Computer Power

The project Advanced Data Mining and Integration Research for Europe (ADMIRE) is designing new methods and tools for comfortable mining and integration of large, distributed data sets. One of the prospective application domains for such methods and tools is the environmental applications domain, which often uses various data sets from different vendors where data mining is becoming increasingly popular and more computer power becomes available. The authors present a set of experimental environmental scenarios, and the application of ADMIRE technology in these scenarios. The scenarios try to predict meteorological and hydrological phenomena which currently cannot or are not predicted by using data mining of distributed data sets from several providers in Slovakia. The scenarios have been designed by environmental experts and apart from being used as the testing grounds for the ADMIRE technology; results are of particular interest to experts who have designed them.

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Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch010 ◽

2016 ◽

pp. 203-214 ◽

Cited By ~ 1

Author(s):

Ahmad Al-Khasawneh

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Cancer Diagnosis ◽

Multilayer Perceptron ◽

Breast Cancer Diagnosis ◽

Machine Learning Algorithms ◽

Data Sets ◽

Self Organizing Map ◽

Intelligent Information ◽

Self Organizing

Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

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Personalized web search on e-commerce using ontology based association mining

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.9487 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 286

Author(s):

B. Sekhar Babu ◽

P. Lakshmi Prasanna ◽

P. Vidyullatha

Keyword(s):

Data Mining ◽

Web Search ◽

Large Data ◽

Association Mining ◽

Data Sets ◽

Data Mining Algorithm ◽

Web Data ◽

Data Mining Technique ◽

Web Data Mining ◽

The Web

In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.

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A MODIFIED KOHONEN SELF-ORGANIZING MAP (KSOM) CLUSTERING FOR FOUR CATEGORICAL DATA

Jurnal Teknologi ◽

10.11113/jt.v78.9275 ◽

2016 ◽

Vol 78 (6-13) ◽

Author(s):

Azlin Ahmad ◽

Rubiyah Yusof

Keyword(s):

Breast Cancer ◽

Categorical Data ◽

Data Sets ◽

Complex Data ◽

Self Organizing Map ◽

Distance Calculation ◽

The Neural Network ◽

Complex Data Sets ◽

Separable Problems ◽

Self Organizing

The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified.

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