Association Rule Mining using Apriori Algorithm for Distributed System: a Survey

Abstract

Data mining technologies provided through Cloud computing is an absolutely necessary characteristic for today's businesses to make proactive, knowledge driven decisions, as it helps them have future trends and behaviors predicted. By implementation of data mining techniques in Cloud will allow users to retrieve meaningful information from virtually integrated data repository that reduces the costs of resources. Research in data mining continues growing in business and in learning organization over coming decades. Association rule mining is a most important area in data mining domain. In association rule mining Apriori algorithm is a very basic and important algorithm as a research point of view. It has some disadvantages it becomes expensive because of frequently scanning of database and it did not support a large amount of raw data and also we have limited resources to implement scalable algorithm. For implementation of scalable Apriori algorithm Map Reduce programming model will be used. Map reduce is a programming model which used to implement and process a scalable raw data. Hadoop provides an open source platform to the map reduce for implementation. But Hadoop have some limited and default task scheduler. So In this paper we have made a survey to implement Apriori algorithm for huge raw data and also overcome Hadoop limitation by using enhanced scheduling algorithm.