In this paper, we present PRICES, an efficient algorithm for mining association rules, which first identifies all large itemsets and then generates association rules. Our approach reduces large itemset generation time, known to be the most time-consuming step, by scanning the database only once and using logical operations in the process. Experimental results and comparisons with the state of t...

In this paper, we propose a ubiquitous product rating system. First we provide an overview of state-of-the-art product recommendation, concluding that current approaches do not support in-store consumers; i.e. consumers on the shop floor. Within shops however is where three out of four buying decisions are made. Hence we propose APriori, a new approach towards mobile product recommendation. APr...

A great research work has been done in last decade in association rules mining (ARM) algorithms . Therefore, various algorithms were proposed to discover frequent item sets and then mine association rules. Apriori algorithm is the most frequently used algorithm for generating association rules. Apriori algorithm has some abuses, such as too many scans of the database, large load of system’s I/O...

With the unceasing development of information and technology in today’s modern society, enterprises’ demand of human resources information mining is getting bigger and bigger. Based on the enterprise human resources information mining situation, this paper puts forward a kind of improved Apriori algorithm based model on the enterprise human resources information mining, this model introduced da...

Data mining is sorting through data to identify patterns and establish relationships. Association rule mining is a well established method of data mining that identifies significant correlations between items in transactional data. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining p...

Frequent Itemset Mining is a well-known concept in data sciences. If we feed frequent itemset miner algorithms with large datasets they become resource hungry fast as their search space explodes. This problem is even more apparent when we try to use them on Big Data. Recent advances in parallel programming provides good solutions to deal with large datasets but they present their own problems w...

Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items usin...

Identification and characterization of gene regulatory binding motifs is one of the fundamental tasks toward systematically understanding the molecular mechanisms of transcriptional regulation. Recently, the problem has been abstracted as the challenge planted (l,d)-motif problem. Previous studies have developed numerous methods to solve the problem. But most of them need to specify the length ...

Frequent item generation is a key approach in association rule mining. The Data mining is the process of generating frequent itemsets that satisfy minimum support. Efficient algorithms to mine frequent patterns are crucial in data mining. Since the Apriori algorithm was proposed to generate the frequent item sets, there have been several methods proposed to improve its performance. But they do ...

A framework is proposed for the detection of reduplication in digital videos of American Sign Language (ASL). In ASL, reduplication is used for a variety of linguistic purposes, including overt marking of plurality on nouns, aspectual inflection on verbs, and nominalization of verbal forms. Reduplication involves the repetition, often partial, of the articulation of a sign. In this paper, the a...