RÉSUMÉ. Nous proposons dans cet article l’algorithme Pascal qui introduit une nouvelle optimisation de l’algorithme de référence Apriori. Cette optimisation est fondée sur le comptage des motifs par inférence, qui utilise le concept de motifs clés. Le support des motifs fréquents non clés peut être inféré du support des motifs clés sans accès à la base de données. Expérimentalement, la comparai...

The issue of educational evaluation has long been a research hotspot. Using big data analysis method to conduct educational evaluation can improve the pertinence and effectiveness of education. Conventional Apriori algorithm has certain limitations in the application of educational evaluation. This paper introduces an improved Apriori-Gen algorithm and describes its application in evaluation of...

Today multilevel association rule mining is an emerging field in data mining. Its main goal is to find hidden information in or between levels of abstraction. It is mainly used for decision making for large data. It focuses on the customer relationship management. Apriori algorithm is mainly used for the multilevel association rule mining. Producing large number of candidate item sets and multi...

We develop protocols for preparing a GHZ state and, in general, a pure multi-partite maximally entangled state in a distributed network with apriori quantum entanglement between agents using classical communication and local operations. We investigate and characterize the minimal combinatorics of the sharing of EPR pairs required amongst agents in a network for the creation of multi-partite ent...

In this paper we investigate the relationship between closed itemset mining, the complete pruning technique and item ordering in the Apriori algorithm. We claim, that when proper item order is used, complete pruning does not necessarily speed up Apriori, and in databases with certain characteristics, pruning increases run time significantly. We also show that if complete pruning is applied, the...

Association rule mining searches for interesting relationship among items in a large data set. Market basket analysis, a typical example of association rule mining, analyzes buying habit of customers by finding association between the different items that customers put in their shopping cart (basket). Apriori algorithm is an influential algorithm for mining frequent itemset for generating assoc...

Association rules are the main technique to determine the frequent itemset in data mining. Apriori algorithm is a classical algorithm of association rule mining. This classical algorithm is inefficient due to so many scans of database. And if the database is large, it takes too much time to scan the database. In this paper, we proposed an Improved Apriori algorithm which reduces the scanning ti...

A data cutting and sorting method (DCSM) is proposed to optimize the performance of data mining. DCSM reduces the calculation time by getting rid of redundant data during the data mining process. In addition, DCSM minimizes the computational units by splitting the database and by sorting data with support counts. In the process of searching for the relationship between metabolic syndrome and li...

Heart disease is a major cause of morbidity and mortality in the modern society. Almost 60% of the world population fall victim to the heart disease. Although significant progress has been made in the diagnosis and treatment of coronary heart disease, further investigation is still needed. Data mining, as a solution to extract hidden pattern from the clinical dataset are applied to a database i...

Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. In this paper, we present the performance comparison of Apriori and FP-growth algorithms. The performance is analyzed based on the execution time...