International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Survey Paper | Data & Knowledge Engineering | India | Volume 12 Issue 3, March 2023


Survey Report on Association Rules Mining for Frequent Item Set Generation

S. V. Subramanyam [3]


Abstract: Data mining is considered to deal with huge amounts of data which are kept in the database, to locate required is survey on association rule mining using information and facts. Innovation of association rules among the huge number of item sets is observed as a significant feature of data mining. The always growing demand of finding pattern from huge data improves the association rule mining. The main purpose of data mining provides superior result for using knowledge base system. Researchers presented a lot of approaches and algorithms for determining association rules. This paper discusses few approaches for mining association rules. Association rule mining approach is the most efficient data mining method to find out hidden or required pattern among the large volume of data. It is responsible to find correlation relationships among various data attributes in a huge set of items in a database. Studying Apriori algorithm, Apriori that is used to extract frequent itemsets from large itemsets. The Apriori algorithm has a limitation of wasting time for scanning the whole database searching on the frequent itemsets. Here we are using an improved apriori algorithm to reduce the time consumed in transaction scanning for candidate itemsets by reducing the number of transactions to be scanned by using smallest minimum support. Another approach discussed is regression technique for pairing the unpaired itemsets also reducing the time consuming of the itemsets.


Keywords: Data mining, Apriori algorithm, minimum support threshold, multiple scan, frequent itemsets, regression


Edition: Volume 12 Issue 3, March 2023,


Pages: 434 - 437


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