Association rule mining is an important data mining problem. Since its inception, different variants of rules has been proposed in the literature. In each case, different attributes (e.g., weight and quantity) are considered to obtain more informative rules. To our knowledge, each proposal is based on the Apriori algorithm that is, in modern context, inefficient. Methods that outperform the Apr...

An Important Problem in Data Mining in Various Fields like Medicine, Telecommunications and World Wide Web is Discovering Patterns. Frequent patterns mining is the focused research topic in association rule analysis. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori Algorith...

Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pas...

Association Rule mining is a sub-discipline of data mining. Apriori algorithm is one of the most popular association rule mining technique. Apriori technique has a disadvantage that before generating a maximal frequent set it generates all possible proper subsets of maximal set. Therefore it is very slow as it requires many database scans before generating a maximal frequent itemset In the meth...

This paper presents the Shape based Movement Pattern (ShaMP) algorithm, an algorithm for extracting Movement Patterns (MPs) from network data that can later be used (say) for prediction purposes. The principal advantage offered by the ShaMP algorithm is that it lends itself to parallelisation so that very large networks can be processed. The concept of MPs is fully defined together with the rea...

Concern about national security has increased after the 26/11 Mumbai attack. In this paper we look at the use of missing value and clustering algorithm for a data mining approach to help predict the crimes patterns and fast up the process of solving crime. We will concentrate on MV algorithm and Apriori algorithm with some enhancements to aid in the process of filling the missing value and iden...

Association Rule Mining is an area of data mining that focuses on pruning candidate keys. An Apriori algorithm is the most commonly used Association Rule Mining. This algorithm somehow has limitation and thus, giving the opportunity to do this research. This paper introduces a new way in which the Apriori algorithm can be improved. The modified algorithm introduces factors such as set size and ...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...

We investigate the application of Galois connections to the identi cation of frequent item sets, a central problem in data mining. Starting from the notion of closure generated by a Galois connection, we de ne the notion of extended closure, and we use these notions to improve the classical Apriori algorithm. Our experimental study shows that in certain situations, the algorithms that we descri...