Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.
KUMAR, K.GANESH; RAMAMOORTHY, H.VIGNESH; KUMAR, M.PREM; and SUDHA, S.
"AN OPTIMIZED ARM SCHEME FOR DISTINCT NETWORK DATA SET,"
International Journal of Computer and Communication Technology: Vol. 6
, Article 9.
Available at: https://www.interscience.in/ijcct/vol6/iss3/9