Disk Resident Taxonomy Mining for Large Temporal Datasets
Journal Title: International Journal of Science Engineering and Advance Technology - Year 2014, Vol 2, Issue 11
Abstract
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 FPtrees 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.
Authors and Affiliations
P. Lakshmi Bhanu| M.Tech Student Department of Computer Science & Engineering, Pragati Engineering College East Godavari (dt), A.P,India, Mrs. N. Leelavathy| Professor & HOD Department of Computer Science & Engineering, Pragati Engineering College East Godavari (dt), A.P,India , Mrs. G. Satya Suneetha| Associate Professor Department of Computer Science & Engineering, Pragati Engineering College East Godavari (dt), A.P,India
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