Sequential mining methods efficiently discover all frequent sequential patterns included in sequential data. These methods use the support, which is the previous criterion that satisfies the Apriori property, to evaluate the frequency. However, the discovered patterns do not always correspond to the interests of analysts, because the patterns are common and the analysts cannot get new knowledge...

| Discovering association rules is one of the most important task in data mining. Many eecient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an eecient algorithm, called Close, based on a new...

In this study a new approach to generate association rules on numeric data is proposed. It has been observed that equal binning techniques are not always useful to convert numerical data into categorical data, specifically in medical data. The proposed approach utilise a modified equal width binning interval technique to discretise continuous valued attributes to nominal based on opinion taken ...

Action recognition in video sequence has been a major challenging research area for number of years. Apriori algorithm and SIFT descriptor based approach for action recognition is proposed in this paper. Here, two phases can be carried out for accurate and updating of action recognition. In the first phase, the input should be the video sequence. For preprocessing the sequences frame can be for...

Association rule mining has recently become a popular area of research. The most expensive step of discovering association rules is to find so-called frequent item sets. The focus of this paper is efficient mining of frequent item sets when the input data contains categorical and quantitative attributes. We propose a new Apriori-like algorithm to solve this problem. The new algorithm, that we h...

Most algorithms for association rule mining are variants of the basic Apriori algorithm One characteristic of these Apriori based algorithms is that candidate itemsets are generated in rounds with the size of the itemsets incremented by one per round The number of database scans required by Apriori based algorithms thus depends on the size of the largest large itemsets In this paper we devise a...

Increased application of structured pattern mining requires a perfect understanding of the problem and a clear identification of the advantages and disadvantages of existing algorithms. Among those algorithms, pattern-growth methods have been shown to have the best performance when applied to sequential pattern mining. However, their advantages over apriori-based methods are not well explained ...

Data Mining techniques are helpful to uncover the hidden predictive patterns from large masses of data. Frequent item set mining also called Market Basket Analysis is one the most famous and widely used data mining technique for finding most recurrent itemsets in large sized transactional databases. Many methods are devised by researchers in this field to carry out this task, some of these are ...

www.ijitam.org Abstract These Apriori Algorithm is one of the wellknown and most widely used algorithm in the field of data mining. Apriori algorithm is association rule mining algorithm which is used to find frequent itemsets from the transactions in the database. The association rules are then generated from these frequent itemsets. The frequent itemset mining algorithms discover the frequent...

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...