An Efficient Range Partitioning Method for Finding Frequent Patterns from Huge Database

Data mining is finding increasing acceptance in science and business areas that need to analyze large amounts of data to discover trends that they could not otherwise find. Different applications may require different data mining techniques. The kinds of knowledge that could be discovered from a database are categorized into association rules mining, sequential patterns mining, classification, and clustering. In this paper we present an efficient range partitioning method for finding frequent pattern from huge database. It is based on key based division for finding the local frequent pattern (LFP). After finding the partition frequent pattern from the subdivided local database, we then find the global frequent pattern from the local database and perform the pruning from the whole database.