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...

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...

Data mining algorithms are essential tools to extract information from the increasing number of large datasets, also called Big Data. However, these algorithms demand huge amounts of computing power to achieve reliable results. Although conventional High Performance Computing (HPC) platforms can deliver such performance, they are commonly expensive and power-hungry. This paper presents a study ...