IJSRSET1844502 | Received : 20 April 2018 | Accepted : 30 April 2018 | March-April-2018 [(4) 4 : 1515-1520] © 2018 IJSRSET | Volume 4 | Issue 4 | Print ISSN: 2395-1990 | Online ISSN : 2394-4099 Themed Section : Engineering and Technology 1515 Mining Negative Association Rules in Distributed Environment Hetal Jadav 1 , Kinjal Thakar 2 1 Research Scholar, M.E., Department of Information and Technology, Silver Oak College of Engineering and Technology, Ahmadabad, Gujarat, India 2 Assistant Professor Department of Information and Technology, Silver Oak College of Engineering and Technology, Ahmadabad, Gujarat, India ABSTRACT In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. the more accurate the process of setting up minimum threshold, the more accurate we find association between data. The data may be positively or negatively relate to each other based on data values. Even though large in number, some data misses some interesting rules and the rules’ quality necessitates further analysis. As a result, we have proposed a hybrid approach based on apriori algorithm for mining frequent item sets. This algorithm will help to discover itemsets which are negatively associated with each other. These association is found on the base of properties of propositional logic, and therefore, requires no background knowledge to generate them. The experiments show that our approach is able to identify meaningful negative association rules within a reasonable execution time. This approach has a new algorithm based on modified apriori, so that users can mine the items without domain knowledge and it can mine the items efficiently when compared to association rules. Keywords: Data Mining, Distributed Database, Negative Association Rule Mining, K-Anonymity. I. INTRODUCTION The main aim of data mining technology is to explore hidden information from large databases. A Real word data coming from many fields. Many data mining techniques are exist such as association rule mining, clustering, classification, regression and so on and have wide applications in the real world for finding the useful data . Association means looking for a relationship between variables or objects. It aims to extract interesting association, correlations or casual structures among the objects association rule mining generates positive and negative rules. Positive rules specify the presence and positive relationship between the objects whereas negative rules specify the absence and negative relationship between the objects and variables. Association rules (ARs), a branch of data mining, have been studied successfully and extensively in many application domains including market basket analysis, intrusion detection, diagnosis decisions support, and telecommunications. Traditionally, the association rule mining algorithms target the extraction of frequent features (itemsets), that is, features boasting high frequency in a transactional database. However, find the association rule in distributed environment is also a interesting research it is important to find the frequent itemset in distributed environment . Many algorithms for generating association rules have been