UNSUPERVISED DATABASE DISCOVERY BASED ON ARTIFICIAL INTELLIGENCE TECHNIQUES
Competitive business pressures and a desire to leverage existing information technology investments have led people to explore the benefits of data mining technology. This technology is designed to help businesses discover hidden patterns in their data - patterns that can help them understand the purchasing behavior of their key customers, detect likely credit card or insurance claim fraud, predict probable changes in financial markets, etc. One approach of data mining is to use some form of supervised discovery. However, supervised discovery limits results as it is necessary to determine in advance what is of interest. This is contra-intuitive to the broadest goals of finding unexpected, interesting things.In this thesis, we make an experimental investigation into autonomous or unsupervised discovery. It is based on the novel paradigm proposed by Dr. L. J. Mazlack [ 1996]. This testable approach is that increasing coherence increases conceptual information; and this in turn reveals previously unrecognized, useful, implicit information. This can be done by recursive partitioning. In order to refine partitioning, we use some artificial intelligence techniques and also proposed the algorithms in clustering and generalizing on both scalar and non-scalar data. The algorithms are tested on some data sets and the results are discussed.
School:University of Cincinnati
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:data mining artificial intelligence database discovery mountain method
Date of Publication:01/01/2002