Data Mining Tool Selection

Data Mining Tool Selection

Christophe Giraud-Carrier (Brigham Young University, USA)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-60566-010-3.ch080
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Abstract

It is sometimes argued that all one needs to engage in Data Mining (DM) is data and a willingness to “give it a try.” Although this view is attractive from the perspective of enthusiastic DM consultants who wish to expand the use of the technology, it can only serve the purposes of one-shot proofs of concept or preliminary studies. It is not representative of the complex reality of deploying DM within existing business processes. In such contexts, one needs two additional ingredients: a process model or methodology, and supporting tools. Several Data Mining process models have been developed (Fayyad et al, 1996; Brachman & Anand, 1996; Mannila, 1997; Chapman et al, 2000), and although each sheds a slightly different light on the process, their basic tenets and overall structure are essentially the same (Gaul & Saeuberlich, 1999). A recent survey suggests that virtually all practitioners follow some kind of process model when applying DM and that the most widely used methodology is CRISP-DM (KDnuggets Poll, 2002). Here, we focus on the second ingredient, namely, supporting tools. The past few years have seen a proliferation of DM software packages. Whilst this makes DM technology more readily available to non-expert end-users, it also creates a critical decision point in the overall business decision-making process. When considering the application of Data Mining, business users now face the challenge of selecting, from the available plethora of DM software packages, a tool adequate to their needs and expectations. In order to be informed, such a selection requires a standard basis from which to compare and contrast alternatives along relevant, business-focused dimensions, as well as the location of candidate tools within the space outlined by these dimensions. To meet this business requirement, a standard schema for the characterization of Data Mining software tools needs to be designed.
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Background

The following is a brief overview, in chronological order, of some of the most relevant work on DM tool characterization and evaluation.

Information Discovery, Inc. published, in 1997, a taxonomy of data mining techniques with a short list of products for each category (Parsaye, 1997). The focus was restricted to implemented DM algorithms.

Elder Research, Inc. produced, in 1998, two lists of commercial desktop DM products (one containing 17 products and the other only 14), defined along a few, yet very detailed, dimensions (Elder & Abbott, 1998; King & Elder, 1998). Another 1998 study contains an overview of 16 products, evaluated against pre-processing, data mining and post-processing features, as well as additional features such as price, platform, release date, etc. (Gaul & Saeuberlich, 1999). The originality of this study is its very interesting application of multidimensional scaling and cluster analysis to position 12 of the 16 evaluated tools in a four-segment space.

In 1999, the Data & Analysis Center for Software (DACS) released one of its state-of-the-art reports, consisting of a thorough survey of data mining techniques, with emphasis on applications to software engineering, which includes a list of 55 products with both summary information along a number of technical as well as process-dependent features and detailed descriptions of each product (Mendonca & Sunderhaft, 1999). Exclusive Ore, Inc. released another study in 2000, including a list of 21 products, defined mostly by the algorithms they implement together with a few additional technical dimensions (Exclusive Ore, 2000).

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