Abstract: Data mining is important and useful tool amongst several systematic tools for extracting data that are accumulated in a database. Frequent pattern mining is one of data mining, which will extract the items that occur more number of times. Data may be of any kind, facts, numbers, or text. In recent years, Organizations are accumulating huge and increasing amounts of data in dissimilar formats and different databases. Frequent itemset mining, which focuses on finding a relationship among data. Change mining, detects and report any considerable changes if occurs in the set of mined itemsets from different time periods. This project extends the vibrant change mining problem, in the framework of frequent itemsets, by exploiting recurrent generalized itemsets to characterize information linked with infrequent patterns. To address this issue, I introduce two novel kinds of vibrant patterns, namely the HIGEN MINER (History Generalized pattern) and NON-REDUNDANT HIGEN MINERs. HIGEN MINER detects the mined frequent itemset, if any items become infrequent during next extraction those changes are focused and avoided by Apriori based support driven approach. NON-REDUNDANT HIGEN MINER, which stores items whose support value is minimum, these infrequent items may become frequent during next extraction. If it remains same, then these infrequent items are discarded.
Keywords: Frequent itemset mining, Change mining, Association Rule Extraction, Higen Miner, Non-Redundent Higen Miner.
Title: DISCOVERY OF FREQUENT AND NON-REDUNDENT ITEMSET USING HIGEN MINER
Author: S.KIRUTHIKA, T.SHEIK YOUSUF
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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