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High Utility ITEMSET Mining from Large Database

Authors(2):

Annusooya. A, T. Seeniselvi
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Frequent itemset mining is one of the main problems in data mining. It has practical importance in a wide range of application areas such as decision support, Web usage mining, bioinformatics, etc. A number of relevant algorithms have been proposed in recent years for the fast access of data from the database. Mining high utility itemsets from a large database refers to the discovery of itemsets with high utility like profits. The proposed work is to mine the high utility items from the large database. The traditional association rule mining algorithm is used to find out the frequently occurring patterns of item sets. Apriori algorithm is used to find the high utility itemset. Data about the products are collected and stored in a database. Whenever customers buy the same product repeatedly the frequent pattern is formed and the infrequent items are separated. The high utility itemset is based on the user-specified utility threshold or it is a low-utility itemset. Admin maintain the entire system process like workers details, user details, product sales, raw materials. Admin can generate report based on the product sales. Admin can generate the Apriori products based on the threshold value. Admin can generate the graph for the frequently purchased products.

Annusooya. A, T. Seeniselvi

Frequent Data Mining (FDM), Association Rule Mining, Apriori Algorithm.

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Publication Details

Published in : Volume 1 | Issue 6 | November-December - 2015
Date of Publication Print ISSN Online ISSN
2015-12-25 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
198-202 IJSRSET151594   Technoscience Academy

Cite This Article

Annusooya. A, T. Seeniselvi, "High Utility ITEMSET Mining from Large Database", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 6, pp.198-202, November-December-2015.
URL : http://ijsrset.com/IJSRSET151594.php

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