Computational Model of Artificial Neural Networks and its Applications in Data Mining

Data remain a very important ingredient required by any organization to make informed decision as it affects operations. Companies have been collecting data from various sources over the decades bringing about a very large volume of data warehouse. Unfortunately, most organizations build databases w...

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Bibliographic Details
Published inArid Zone Journal of Engineering Technology and Environment Vol. 16; no. 2; pp. 243 - 254
Main Authors J. Abah, E. S. Alu, A. E. Chahari
Format Journal Article
LanguageEnglish
Published University of Maiduguri 01.06.2020
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Summary:Data remain a very important ingredient required by any organization to make informed decision as it affects operations. Companies have been collecting data from various sources over the decades bringing about a very large volume of data warehouse. Unfortunately, most organizations build databases which are redundant and never used for any meaningful thing. While few companies use the data collected in their databases when taking strategic decisions others barely do same. However, for an organization to immensely derive benefits from the massive data warehouse, there is the need for an effective and efficient means of analysing the data with a view to extracting meaningful knowledge that is sufficient to achieve organizational goal. To achieve this, Artificial Neural Network (ANN) technique through the concept known as data mining is presented. The paper reviewed artificial neural network technique for data mining, examines the computational model behind this technique and analysed its use and application as a predicting or forecasting tool. Results shows that ANN’ has capability in data management, analysis and able to provide desirable knowledge for management decision making processes. It is therefore recommended that data mining tools like ANN and others be applied to organization’s databases which hitherto have not been minned in order to provide management with intelligence for decision making.
ISSN:2545-5818
2545-5818