Mining Frequent Pattern Form Large Dynamic Database with Time Granularities to Improve Efficiency
Pradnya A. Shirsath1, Vijay Kumar Verma2
1Pradnya A. Shirsath, Department of Computer Science and Engineering, Lord Krishna College of Technology, Indore, (M.P), India.
2Prof. Vijay Kumar Verma, Department of Computer Science and Engineering, Lord Krishna College of Technology, Indore, (M.P), India.
Manuscript received on September 30, 2013. | Revised Manuscript received on October 13, 2013. | Manuscript published on October 30, 2013. | PP: 368-372 | Volume-3, Issue-1, October 2013. | Retrieval Number: A2291103113/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Incremental algorithms can manipulate the results of earlier mining to derive the final mining output in various businesses [1, 2, 3]. This study proposes a new algorithm, called the new approach for efficiently incrementally mining frequent pattern from large Dynamic database. Proposed approach is a backward method that only requires scanning incremental database. Rather than rescanning the original database for some new generated frequent item sets in the incremental database, we add the occurrence counts of newly generated frequent item sets and delete infrequent item sets obviously. Thus, new proposed approach need not rescan the original database and to discover newly generated frequent item sets. Proposed approach generates fewer candidates, reduces complex calculation and has good scalability as compared to the previous methods.
Keywords: Large Dynamic database, incremental Proposed approach, generated.