Business data mining — a machine learning perspective
Information & ManagementPublished 1 December 2001
Indranil Bose, Radha K. Mahapatra
Citations429
SJR quartileQ1
SJR score2.92
SNIP2.74
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TL;DR
An overview of machine learning techniques is provided and their strengths and weaknesses in the context of mining business data are discussed.
Abstract
The objective of this paper is to inform the information systems (IS) manager and business analyst about the role of machine learning techniques in business data mining. Data mining is a fast growing application area in business. Machine learning techniques are used for data analysis and pattern discovery and thus can play a key role in the development of data mining applications. Understanding the strengths and weaknesses of these techniques in the context of business is useful in selecting an appropriate method for a specific application. The paper, therefore, provides an overview of machine learning techniques and discusses their strengths and weaknesses in the context of mining business data. A survey of data mining applications in business is provided to investigate the use of learning techniques. Rule induction (RI) was found to be most popular, followed by neural networks (NNs) and case-based reasoning (CBR). Most applications were found in financial areas, where prediction of the future was a dominant task category.
Keywords
Computer ScienceBusiness, Management and Accounting
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