Disk Resident Taxonomy Mining for Large Temporal Datasets

P.Lakshmi Bhanu, N. Leelavathy, G.Satya Suneetha


Mining patterns under constraints in large data is a significant task to advantage from the multiple uses of the patterns embedded in these data sets. It is obviously a difficult task because of the exponential growth of the search space. Extracting the patterns under various kinds of constraints in such type of data is a challenging research. First, a memory-based, efficient pattern-growth algorithm, Forest Mine, is proposed for mining frequent patterns for the data sets and then consolidating global frequent patterns. For dense data sets, Forest-mine is integrated with FP-Tree dynamically by detecting the swapping condition and constructing FP-trees for efficient mining. Such efforts ensure that forest mine is scalable in both large and medium sized databases and in both sparse and dense data sets.


Mining Frequent Itemsets from Secondary Memory, G¨osta Grahne and Jianfei Zhu, Concordia University,Montreal, Canada [2] “Mining Frequent δ-Free Patterns in Large Databases”, Céline Hébert, Bruno Crémilleux, Discovery Science,Lecture Notes in Computer Science Volume 3735, 2005, pp 124-136

“H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases”, Jian Pei, Jiawei Han, Hongjun Lu✄ , Shojiro Nishio, Shiwei Tang, Dongqing Yang

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