• Dhanashree R. Kolhe
• Dr.Varsha Namdeo

Algorithms are used on very big data sets with high dimensionality. Therefore fast processing can be applied for mining using association rules. The process of association rule mining consists of identifying frequent item sets and generating rules from the frequent item data sets. Finding frequent item sets is more complex in terms of CPU power consumption and computing resources utilization. Thus majority of parallel apriori algorithms focus on parallelizing the process of discovering frequent item set. The computation of frequent item sets mainly consists of creating the candidates and counting them. In parallel frequent item sets mining algorithms addresses the issue of distributing the candidates among processors such that their creation and counting is effectively parallelized. This paper presents comparative study of these algorithms.

Parallel data mining, frequent item sets, association rules, apriori algorithm

"DYNAMIC REQUEST REDIRECTION FOR MINING SYSTEM SERVICES UNDER HETEROGENEOUS DISTRIBUTED SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.19-23, July-2018, Available :http://www.jetir.org/papers/JETIR1807473.pdf

"DYNAMIC REQUEST REDIRECTION FOR MINING SYSTEM SERVICES UNDER HETEROGENEOUS DISTRIBUTED SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp19-23, July-2018, Available at : http://www.jetir.org/papers/JETIR1807473.pdf