Data mining, also known as Knowledge Discovery in Databases (KDD) is one of the most important and interesting research areas in 21 century. Frequent pattern discovery is one of the important techniques in data mining. The application includes Medicine, Telecommunications and World Wide Web. Nowadays frequent pattern discovery research focuses on finding co-occurrence relationships between item...

Rsumm La ddcouverte des rgles d'association est l'un des principaux probllmes de l'extraction de connaissances dans les bases de donnnes. De nombreux algorithmes eecaces ont tt proposss, dont les plus remarquables sont Apriori, l'algorithme de Mannila, Partition, Sampling et DIC. Ces derniers sont tous basss sur la mmthode de recherche de Apriori: l''lagage du treillis des parties (treillis des...

Rsumm La ddcouverte des rgles d'association est l'un des principaux probllmes de l'extraction de connaissances dans les bases de donnnes. De nombreux algorithmes eecaces ont tt proposss, dont les plus remarquables sont Apriori, l'algorithme de Mannila, Partition, Sampling et DIC. Ces derniers sont tous basss sur la mmthode de recherche de Apriori: l''lagage du treillis des parties (treillis des...

Fully homomorphic encryption (FHE) enables secret computations. Users can perform computation using data encrypted with FHE without decryption. Uploading private to a public cloud has the risk of leakage, which makes many users hesitant utilize cloud. avoids this risk, while still providing computing power In cases, are stored in HDDs because size increases significantly when is used. One impor...

Keputusan merupakan suatu hal yang sangat berpengaruh dalam proses menghadapi alternatif yag dipilih. Berbagai kendala memilih penjurusan sesuai dengan kriteria memang cukup membingungkan. Salah satu adalah calon peserta didik baru tidak mengikuti minat dan kemampuannya, justru pilihan teman-temannya. Tujuan penelitian ini untuk menentukan konsentrasi akan diambil oleh menggunakan algoritma Apr...

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. ...

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms. The first and arguably most influential algorithm for efficien...

We will discuss DF , the depth £rst implementation of APRIORI as devised in 1999 (see [8]). Given a database, this algorithm builds a trie in memory that contains all frequent itemsets, i.e., all sets that are contained in at least minsup transactions from the original database. Here minsup is a threshold value given in advance. In the trie, that is constructed by adding one item at a time, eve...

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...