Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items

Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.