Integrated genetic - fuzzy approach for items with single minimum support using 3-dimensional k-means clustering

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values. Transactions with quantitative values are however commonly seen in real-world applications. The fuzzy concepts are used to represent item importance, item quantities, minimum supports and minimum confidences. Each attribute uses only the linguistic term with the maximum cardinality in the mining process. The number of items is thus the same as that of the original attributes, making the processing time reduced. It uses a combination of large 1-itemsets and membership-function suitability to evaluate the fitness values of chromosomes. The calculation for large 1-itemsets could take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this system, an enhanced approach, called the cluster-based fuzzy-genetic mining algorithm. It divides the chromosomes in a population into clusters by the 3-dimensional k-means clustering approach and evaluates each individual according to both cluster and their own information. A genetic-fuzzy data-mining algorithm for extracting fit membership functions and multilevel association rules with its confidence from quantitative transactions is shown.