Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one pre...

We formulate a general Association rule mining model for extracting useful information from very large databases. An interactive Association rule mining system is designed using a combination of genetic algorithms and a modified a-priori based algorithm. The association rule mining problem is modeled as a multi-objective combinatorial problem which is solved using genetic algorithms. The combin...

A novel approach is presented for mining weighted association rules (ARs) from binary and fuzzy data. We address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance w.r.t some user defined criteria. Most works on weighted association rule mining so far struggle with invalid downward c...

The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the disco...

-Data mining includes number of techniques like clustering, classification, sequential patterns, association rules and etc. Association rule mining is a technique for mining interesting rules from large databases for further analysis. Association rule mining includes two-step approach for extracting the rules. These two steps require many database scan and use support and confidence as the thre...

This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for ‘Association Rule Tree’, builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized “Separate and Conquer” algorithm suitable for Data Mining applications because it makes use of efficient and sc...

Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and tra...

Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. We purpose a Genetic Algorithm to generate high quality Association Rules with four metrics they are confidence, completeness, interestingness and comprehensibility. These metrics are combined as an objective fitness function. Fitness function eva...

Association rule mining finds interesting associations and/or correlation relationships among large set of data items. However, when the number of association rules become large, it becomes less interesting to the user. It is crucial to help the decision-maker with an efficient postprocessing step in order to select interesting association rules throughout huge volumes of discovered rules. This...

Data mining is a technique of analyzing the dataset such that the final conclusion can be accessed easily and quickly from the dataset. Here in this paper association rule mining technique is implemented for the analysis of agricultural dataset. The idea is to apply association rule mining technique for generating rules and establishing a relationship between them .So as to enhance crop product...