Association mining in time-varying domains
Intelligent Data Analysis
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Data mining is a relatively new area of computer science that has received increased attention in the last decade. The basic goal of data mining is to extract new and useful knowledge from vast amounts of data. A popular subfield of data mining is the area of association mining that searches for frequently co-occurring items in a market-basket type database. A market basket is the list of items a customer purchases at the register. Of course, this type of database is not limited to marketing only; other possible areas include analysis of web data, astronomical data, stock market analysis, medical field, and many others. The basic tenet of this work is that association patterns evolve over time due to fashion, season, or the introduction of new products. The dissertation proposes a novel window-based system to induce a model of the current state of the database. The main contributions of this work are three groups of heuristics for detecting change in context, and three operators for adjusting the window after a change has been detected. The dissertation experimentally examines the system's behavior for all combinations of the change detection heuristics and the window adjustment operators in two kinds of domains. The domains of the first kind abruptly change their properties while the domains of the second kind gradually evolve from one context to another. The dissertation shows that a system with a variable-size window achieves higher accuracy of context approximation, as compared to a system with no window or a fixed-size-window system. The change detection heuristics proposed in this work achieve the same accuracy as a heuristic coming from an earlier work, but with one to two orders of magnitude lower computational costs.