Published Paper Details:

Abstract: Cloud computing has demonstrated that processing very large datasets over commodity clusters can be done by giving the right programming model. Cloud can be meant as an infrastructure that provides resources and/or service over the internet. A cloud can be a storage cloud that provides block or file based storage service or it can be a compute cloud that provides computational services. Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Mining association rules is one of the most important aspects in data mining. Association rules are dependency rules which predict occurrence of an item based on occurrences of other items. Apriori is the best-known algorithm to mine association rules. The Apriori algorithm had a major problem of multiple scans through the entire data. It required a lot of space and time. The main issue with data mining techniques is that the space required for the item set and there operations are very huge. If we combine data mining techniques with cloud computing environment, then we can rent the space from the cloud providers on demand. This solution can solve the problem of huge space with better application usage in low cost. We can apply data mining techniques without taking any consideration of space. The modification in our paper suggests that we do not scan the whole database to count the support for every attribute. This is possible by keeping the count of minimum support and then comparing it with the support of every attribute. The support of an attribute is counted only till the time it reaches the minimum support value. using sector/sphere framework with association rules