Results 31 to 40 of about 2,811 (218)
Finding the True Frequent Itemsets [PDF]
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014 Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $θ$ of a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the understanding of the underlying process that ...
Riondato, Matteo, VANDIN, FABIO
openaire +4 more sources
Knowledge Engineering and Data Science, 2020 Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset.
Adie Wahyudi Oktavia Gama +1 more
doaj +1 more source
arules - A Computational Environment for Mining Association Rules and Frequent Item Sets [PDF]
, 2005 Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Bettina Grün +6 more
core +1 more source
Frequent Itemset Mining for Big Data [PDF]
, 2017 Traditional data mining tools, developed to extract actionable knowledge from data, demonstrated to be inadequate to process the huge amount of data produced nowadays.
Pulvirenti, Fabio
core +1 more source
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, 2016 AbstrakAlgoritma yang umum digunakan dalam proses pencarian frequent itemset (data yang paling sering muncul) adalah Apriori. Tetapi Algoritma Apriori mempunyai memiliki kekurangan yaitu membutuhkan waktu yang lama dalam proses pencarian frequent itemset.
Wirdah Choiriah
doaj +3 more sources
FIsViz: A Frequent Itemset Visualizer [PDF]
, 2008 Since its introduction, frequent itemset mining has been the subject of numerous studies. However, most of them return frequent itemsets in the form of textual lists. The common cliche that "a picture is worth a thousand words" advocates that visual representation can enhance user understanding of the inherent relations in a collection of objects such ...
Carson Kai-Sang Leung +2 more
openaire +1 more source
The MapReduce Model on Cascading Platform for Frequent Itemset Mining
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2018 The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models.
Nur Rokhman, Amelia Nursanti
doaj +1 more source
A Bitmap Approach for Mining Erasable Itemsets
IEEE Access, 2021 Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of ...
Tzung-Pei Hong +4 more
doaj +1 more source
Frequent Itemsets for Genomic Profiling [PDF]
, 2005 Frequent itemset mining is a promising approach to the study of genomic profiling data. Here a dataset consists of real numbers describing the relative level in which a clone occurs in human DNA for given patient samples. One can then mine, for example, for sets of samples that share some common behavior on the clones, i.e., gains or losses.
Jeannette M. de Graaf +3 more
openaire +1 more source
, 2017 Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear ...
Jerry Chun‐Wei Lin +11 more
core +1 more source