• G.Srilatha
• Dr. N.Subhash Chamdra
• A.Vishwanath

In many applications the progress of association rules has made very practical in the vein of market basket analysis, decision making, and other varied fields. As the records are continuously added in to the transactional databases and also outdated transactions are useless and are removed, new essential applications need incremental mining. Incremental mining deals with the discovery of association rules acquired from mining of earlier stored databases and incremented databases without scanning the earlier mined databases. Based on this, mining positive and negative association rules is an important research topic. In this paper, proposed an algorithm (INC_PNAR) based on a tree based approach(INC_FIFIT) as a data structure to hold frequent and infrequent itemests, which updates the INC_FIFIT when new transactions are inserted and mine both positive and negative association rules. With a Yule’s Coefficient measure, the INC_PNAR algorithm generates all valid positive and negative association rules

Data Mining, Frequent Itemsets, Infrequent Itemsets, Positive Association Rules, Negative Association Rules

"MINING ASSOCIATION RULES IN INCREMENTAL MINING USING INC_PNAR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.617-624, April-2019, Available :http://www.jetir.org/papers/JETIRAU06096.pdf

"MINING ASSOCIATION RULES IN INCREMENTAL MINING USING INC_PNAR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp617-624, April-2019, Available at : http://www.jetir.org/papers/JETIRAU06096.pdf