Please use this identifier to cite or link to this item:
|Title:||Mining closed colossal frequent patterns from high-dimensional dataset: Serial versus parallel framework|
|Citation:||Advances in Intelligent Systems and Computing, 2018, Vol.518, , pp.317-326|
|Abstract:||Mining colossal patterns is one of the budding fields with a lot of applications, especially in the field of bioinformatics and genetics. Gene sequences contain inherent information. Mining colossal patterns in such sequences can further help in their study and improve prediction accuracy. The increase in average transaction length reduces the efficiency and effectiveness of existing closed frequent pattern mining algorithm. The traditional algorithms expend most of the running time in mining huge amount of minute and midsize patterns which do not enclose valuable information. The recent research focused on mining large cardinality patterns called as colossal patterns which possess valuable information. A novel parallel algorithm has been proposed to extract the closed colossal frequent patterns from high-dimensional datasets. The algorithm has been implemented on Hadoop framework to exploit its inherent distributed parallelism using MapReduce programming model. The experiment results highlight that the proposed parallel algorithm on Hadoop framework gives an efficient performance in terms of execution time compared to the existing algorithms. � Springer Nature Singapore Pte Ltd. 2018.|
|Appears in Collections:||2. Conference Papers|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.