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IRFP-tree: Intersection Rule Based FP-tree
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 Title & Authors
IRFP-tree: Intersection Rule Based FP-tree
Lee, Jung-Hun;
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For frequency pattern analysis of large databases, the new tree-based frequency pattern analysis algorithm which can compensate for the disadvantages of the Apriori method has been variously studied. In frequency pattern tree, the number of nodes is associated with memory allocation, but also affects memory resource consumption and processing speed of the growth. Therefore, reducing the number of nodes in the tree is very important in the frequency pattern mining. However, the absolute criteria which need to order the transaction items for construction frequency pattern tree has lowered the compression ratio of the tree nodes. But most of the frequency based tree construction methods adapted the absolute criteria. FP-tree is typically frequency pattern tree structure which is an extended prefix-tree structure for storing compressed frequent crucial information about frequent patterns. For construction the tree, all the frequent items in different transactions are sorted according to the absolute criteria, frequency descending order. CanTree also need to absolute criteria, canonical order, to construct the tree. In this paper, we proposed a novel frequency pattern tree construction method that does not use the absolute criteria, IRFP-tree algorithm. IRFP-tree(Intersection Rule based FP-tree). IRFP-tree is constituted with the new paradigm of the intersection rule without the use of the absolute criteria. It increased the compression ratio of the tree nodes, and reduced the tree construction time. Our method has the additional advantage that it provides incremental mining. The reported test result demonstrate the applicability and effectiveness of the proposed approach.
Big Data Analysis;Data Mining;Frequent Pattern Analysis;FP-Tree;DRFP-Tree;
 Cited by
PPFP(Push and Pop Frequent Pattern Mining): A Novel Frequent Pattern Mining Method for Bigdata Frequent Pattern Mining, KIPS Transactions on Software and Data Engineering, 2016, 5, 12, 623  crossref(new windwow)
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