Synthesizing Model for Clustering Frequent Data Items in Multi-Database
R. Suganthi1, P. Kamalakannan2
1Asst Prof. Mrs. R. Suganthi, Department of Computer Applications, Valluvar college of Arts and Science, Karur, Tamil Nadu, India.
2Dr. P. Kamalakannan, Department of Computer Science, Government Arts College , Namakkal, India.
Manuscript received on 30 January 2015 | Revised Manuscript received on 12 February 2015 | Manuscript Published on 28 February 2015 | PP: 57-59 | Volume-4 Issue-9, February 2015 | Retrieval Number: I1980024915/15©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: Mainly, most of the large organizations have numerous databases and they do process and transact over the multiple branch database. The important issue of the multi database is selecting the frequent items from various branch databases and forwarding the items to head quarters to take the decision among all kinds of patterns. Here global decision is important role in head quarter level and some steps are followed to take critical decision in top level. First step is synthesizing high frequency item set based on local item set. Second step is to measure the association [13] among various items listed under high frequency. And the accuracy level of data set is improved by using the synthesizing and clustering algorithm.
Keywords: Multi database, Synthesizing patterns, local pattern analysis, patterns
Scope of the Article: Clustering