Informational Data Mining: A Survey from 2000 to 2010

Informational Data Mining: A Survey from 2000 to 2010

Feyza Gürbüz (University of Erciyes, Turkey) and Fatma Gökçe Önen (University of Erciyes, Turkey)
DOI: 10.4018/978-1-4666-3946-1.ch005


The previous decades have witnessed major change within the Information Systems (IS) environment with a corresponding emphasis on the importance of specifying timely and accurate information strategies. Currently, there is an increasing interest in data mining and information systems optimization. Therefore, it makes data mining for optimization of information systems a new and growing research community. This chapter surveys the application of data mining to optimization of information systems. These systems have different data sources and accordingly different objectives for knowledge discovery. After the preprocessing stage, data mining techniques can be applied on the suitable data for the objective of the information systems. These techniques are prediction, classification, association rule mining, statistics and visualization, clustering and outlier detection.
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2. Information Systems Optimization

Information systems ensure saved significant documents from the everyday observations in an organization. Thanks to IS, the management departments of organizations can reach the information they look for easily and on time. The more IS are used, the more reliable data can be reached. Information systems are increasingly important for measuring and improving quality (Bates et al., 1999).

Technology has created new information alternatives that may influence the way information systems users make decisions as O’Donnell and David (2000) mentioned as in Figure 1.

Figure 1.

Decision-making framework for information systems (AIS: accounting information systems) (O’Donnell & David, 2000)


As Kogut and Zander (1992) mentioned, knowledge is very important for organizations, this knowledge helps to collect a technology, service, or product across locations, and it extends beyond individual knowledge, interest, and agendas. The reuse of knowledge collected is also vital for organizations for economic stability. Information systems allow knowledge to be stored, mediated, searched, and reused at lower cost (Krogh, 2009). The other economic benefits of IS are: the reduction of operational costs through process improvement (by automating, streamlining or re-engineering capital and labor intensive activities), and the improvement of resource allocation by providing more accurate and timely information to decision makers (O’Connor & Martinsons, 2006).

While there has been advances in technology and refinement in most organizations’ structure, IS integration is still extremely complex mainly due to the comprehensive technologies of different ages owned by different process members (Wainwright, Reynolds & Argument, 2003). People still face up some difficulties while they try to obtain the information they expected from an IS tool. An optimizing study can be useful for increasing efficiency of the IS.

An optimization study of the IS process created by Wainwright, Reynolds and Argument (2003) is shown in Figure 2, it has three main stages: audit, assessment and suggestion. In audit stage the existing techniques about production, marketing, sales etc. of the organization are examined. Due to the audit stage, the organization can be aware of the existing situation for system’s efficiency.

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