Employing Neural Networks in Data Mining

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Author(s): Mohamed Salah Hamdi (UAE University, UAE)
Copyright: 2005
Pages: 5
Source title: Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-557-3.ch082

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

Data-mining technology delivers two key benefits: (i) a descriptive function, enabling enterprises, regardless of industry or size, in the context of defined business objectives, to automatically explore, visualize, and understand their data and to identify patterns, relationships, and dependencies that impact business outcomes (i.e., revenue growth, profit improvement, cost containment, and risk management); (ii) a predictive function, enabling relationships uncovered and identified through the data-mining process to be expressed as business rules or predictive models. These outputs can be communicated in traditional reporting formats (i.e., presentations, briefs, electronic information sharing) to guide business planning and strategy. Also, these outputs, expressed as programming code, can be deployed or hard wired into business-operating systems to generate predictions of future outcomes, based on newly generated data, with higher accuracy and certainty.

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Employing Neural Networks in Data Mining

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