Efficiently mining frequent patterns from dense datasets using a cluster of computers
MetadataShow full item record
Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we present an algorithm for finding complete frequent patterns from very large dense datasets in a cluster environment. The data needs to be distributed to the nodes of the cluster only once and the mining can be performed in parallel many times with different parameter settings for minimum support. The algorithm is based on a master-slave scheme where a coordinator controls the data parallel programs running on a number of nodes of the cluster. The parallel program was executed on a cluster of Alpha SMPs. The performance of the algorithm was studied on small and large dense datasets. We report the results of the experiments that show both speed up and scale up of our algorithm along with our conclusions and pointers for further work.
The original publication is available at http://www.springerlink.com
The link to this chapter is: http://springerlink.metapress.com/content/epe5cygrxmncd1mb/fulltext.pdf
Showing items related by title, author, creator and subject.
Techniques for improving clustering and association rules mining from very large transactional databasesLi, Yanrong (2009)Clustering and association rules mining are two core data mining tasks that have been actively studied by data mining community for nearly two decades. Though many clustering and association rules mining algorithms have ...
Ren, H.; Topal, Erkan (2014)Typical mine planning process includes creating a mining block model, applying the ultimate pit limit analysis and creating mining cuts for production scheduling. Mixed integer linear programming (MILP) has been used ...
Mai, N.; Topal, Erkan; Erten, Oktay (2018)© The Southern African Institute of Mining and Metallurgy, 2018. Mathematical programming has been applied to optimizing open pit mine planning problems since the early 1960s. Nonetheless, it still remains challenging to ...