Large Dataset Compression Approach Using Intelligent Technique

Data clustering is a process of putting similar data into groups. A clustering algorithms partition data set into several groups such that the similarity within a group is larger than among groups. Association rule is one of the possible methods for analysis of data. The association rules algorithm generates a huge number of association rules, of which many are redundant. The main idea of this paper is to compress large database by using clustering techniques with association rule algorithms. In the first stage, the database is compressed by using clustering techniques followed by association rules algorithm. Adaptive k-means clustering algorithm is proposed with apriori algorithm. Due to many experiments by using the adaptive k-means algorithm and apriori algorithm together it gives better compression ratio and smaller compressed file size than the compression ratio and compressed file size that are given from using each algorithm alone. Several experiments were made in several different sizes of database. The apriori algorithm increases the compression ratio of the adaptive kmeans algorithm when hey are used together but it takes more compression time than the adaptive kmeans takes. These algorithms are presented and their results are compared.