Attribute transformations for data mining II: Applications to economic and stock market data

✍ Scribed by Joseph Tremba; Tsau Young (T.Y.) Lin

• Publisher: John Wiley and Sons
• Year: 2002
• Tongue: English
• Weight: 80 KB
• Volume: 17
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/int.10018

✦ Synopsis

The effects of attribute transformations have been examined theoretically in part I of this article. This is part II, and its focus is on applications. Specific linear transformations, which have statistical meaning, are applied to a selected set of economic and stock market data. The data are selected from the computer, semiconductor, and semiconductor equipment industries. The main data mining tool is the rough set based software, DataLogic/R+, augmented with programs that perform linear transformations, concept generalization, and so on. Some useful "predictive" rules are discovered. Here, "predictive" is used in the sense that the logical patterns involve time elements. We should note that even in such simple cases, a trail-and-error approach is necessary for finding the right transformation.