A Framework for association rule mining of distributed data

Abstract

The exponential rise in the collected data generated an essential need for new techniques that can convert this huge amount of data into useful knowledge. Consequently,Data Mining (DM) has become a powerful technology focusing on the most important information in the massive data. DM extracts the interesting data patterns from newline large databases using computational techniques/tools. The classical central data ware- newline house(DW) based DM approach is ine ective or infeasible because of heavy storage, computational and communication costs involved in managing data from the ever increasing and privacy-sensitive distributed resources. Distributed Data Mining (DDM) is emerged newline as an active sub-area of DM research. DDM is concerned with application of classical newline DM procedures in a distributed computing environment to effectively utilize the available newline resources. newline Association Rules (ARs) are used to discover the associations among frequent itemsets newline in a database. Association Rule Mining (ARM) today is one of the most important aspects newline of DM task. In ARM all the strong association rules are generated from the frequent newline itemsets. Distributed Association Rule Mining (DARM) generates the globally strong newline association rules from the global frequent itemsets in a distributed environment for the newline global decision making. newline Agent mining also known as Agent enriched DM, is an emerging interdisciplinary area newline that integrates agent technology, DM, machine learning. Most of the existing agent based frameworks for DARM task are only prototype model and lacks the appropriate underlying Agent Execution Environment (AEE), scalability, privacy preserving techniques, global knowledge and implementation using a real datasets especially in bio-informatics domain. newlineBio-informatics or computational molecular biology aims at automated analysis and the newlinemanagement of high-throughput biological data as well as modeling and simulation of newlinecomplex biological systems.