Data Mining is considered as a power tool to discover knowledge such as in form of association rule that is useful in business domains for medical diagnosis, Customer Relationship Management (CRM) etc. Yet, there are two drawbacks in conventional mining. Since most of the techniques performs plane-mining based on priori-defined patterns in the data-warehouse as a whole, so a fully re-scan must ...

The important issue for association rules generation is the discovery of frequent itemset in data mining. Most of the existing real time transactional databases are multidimensional in nature. The classical Apriori algorithm mainly concerned with handling single level, single-dimensional boolean association rules. These algorithms scan the transactional databases or datasets many times to find ...

Associative-classification is a promising classification method based on association-rule mining. Significant amount of work has already been dedicated to the process of building a classifier based on association rules. However, relatively small amount of research has been performed in association-rule mining from multi-label data. In such data each example can belong, and thus should be classi...

The important issue for association rules generation is the discovery of frequent itemset in data mining. Most of the existing real time transactional databases are multidimensional in nature. The classical Apriori algorithm mainly concerned with handling single level, single-dimensional boolean association rules. These algorithms scan the transactional databases or datasets many times to find ...

Generalized association rule mining is an extension of traditional association rule mining to discover more informative rules, given a taxonomy. In this paper, we describe a formal framework for the problem of mining generalized association rules. In the framework, The subset-superset and the parent-child relationships among generalized itemsets are introduced to present the different views of ...

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...

Data Mining is often considered as a process of automatic discovery of new knowledge from large databases. However the role of the human within the discovery process is essential. Domain knowledge consists of information about the data that is made available by the domain experts. Such knowledge constrains the search space and enhances the performance of the mining process. We have developed an...

Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard-partitioning techniques where each object is assigned to one cluster. In this paper we propose an overlapping algorithm MCOKE which allows objects to belong to one or more clusters. The algorithm is different from fuzzy clustering techniques because objects that overlap are assigned a membersh...