Association rule mining is considered as a Major technique in data mining applications. It reveals all interesting relationships, called associations, in a potentially large database. However, how interesting a rule is depends on the problem a user wants to solve. Existing approaches employ different parameters to guide the search for interesting rules. Class association rules which combine ass...

Classification rule mining is used to discover a small set of rules in the database to form an accurate classifier. Association rules mining are used to reveal all the interesting relationship in a potentially large database. For association rule mining, the target of the discovery is not predetermined, while for classification rule mining there is one and only one predetermined target. These t...

Association rule mining play important rule in market data analysis and also in medical diagnosis of correlated problem. For the generation of association rule mining various technique are used such as Apriori algorithm, FP-growth and tree based algorithm. Some algorithms are wonder performance but generate negative association rule and also suffered from Superiority measure problem. In this pa...

Association rule mining has attracted wide attention in both research and application area recently. Mining multilevel association rules is one of the most important branch of it. This paper introduces an improved apriori algorithm so called FP-growth algorithm that will help resolve two neck-bottle problems of traditional apriori algorithm and has more efficiency than original one. New FP tree...

Association rule mining is a data mining technique to extract interesting relationships from large datasets [1, 2]. The efficiency of association rule mining algorithms has been a challenging research area in the domain of data mining [3]. Frequent pattern discovery, the task of finding sets of items that frequently occur together in a dataset is the most resource consuming phase of the rule mi...

Association rule mining searches for interesting relationship among items in a large data set. Market basket analysis, a typical example of association rule mining, analyzes buying habit of customers by finding association between the different items that customers put in their shopping cart (basket). Apriori algorithm is an influential algorithm for mining frequent itemset for generating assoc...

The discovery of association rule acquire an imperative role in data mining since its inception, which tries to find correlation among the attributes in a database. Classical algorithms/procedures meant for Boolean data and they suffer from sharp boundary problem in handling quantitative data. Thereby fuzzy association rule (i.e., association rule based on fuzzy sets) with fuzzy minimum support...

Association rule mining is an important branch of data mining research that aims to extract important relations from data. In this paper, we develop a new framework for mining association rules based on minimal predictive rules (MPR). Our objective is to minimize the number of rules in order to reduce the information overhead, while preserving and concisely describing the important underlying p...