Results 111 to 120 of about 719,228 (260)

Algorithms for frequent itemset mining: a literature review

Artificial Intelligence Review, 2018
Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage.
Chin-Hoong Chee, J. Jaafar, I. Aziz, M. H. Hasan, W. Yeoh   +4 more
semanticscholar   +1 more source

Frequent itemset mining: technique to improve eclat based algorithm

, 2019
In frequent itemset mining, the main challenge is to discover relationships between data in a transactional database or relational database. Various algorithms have been introduced to process frequent itemset.
Jalil, Masita Abdul, Masita Abdul Jalil, Mahadi Man, Man, Mahadi   +3 more
core   +1 more source

Frequent Itemset Mining in Big Data With Effective Single Scan Algorithms

IEEE Access, 2018
This paper considers frequent itemsets mining in transactional databases. It introduces a new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an alternative approach (EA-SSFIM), as well as a parallel implementation on ...
Y. Djenouri, D. Djenouri, Jerry Chun-Wei Lin, Asma Belhadi   +3 more
semanticscholar   +1 more source

Practical Approaches for Mining Frequent Patterns in Molecular Datasets

Bioinformatics and Biology Insights, 2016
Pattern detection is an inherent task in the analysis and interpretation of complex and continuously accumulating biological data. Numerous itemset mining algorithms have been developed in the last decade to efficiently detect specific pattern classes in
Stefan Naulaerts, Sandy Moens, Kristof Engelen, Wim Vanden Berghe, Bart Goethals, Kris Laukens, Pieter Meysman   +6 more
doaj   +1 more source

Similarity processing in multi-observation data [PDF]

, 2012
Many real-world application domains such as sensor-monitoring systems for environmental research or medical diagnostic systems are dealing with data that is represented by multiple observations.
Bernecker, Thomas, Thomas Bernecker
core  

mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets

, 2017
High utility itemset mining is the problem of finding sets of items whose utilities are higher than or equal to a specific threshold. We propose a novel technique called mHUIMiner, which utilises a tree structure to guide the itemset expansion process to
Peng, AY, Alex Yuxuan Peng, Riddle, Patricia, Yun Sing Koh, Koh, Yun Sing, Patricia Riddle   +5 more
core   +1 more source

Geometrically Inspired Itemset Mining

, 2008
In our geometric view, an itemset is a vector (itemvector) in the space of transactions. The support of an itemset is the generalized dot product of the participating items.
Florian Verhein, Sanjay Chawla
core  

Mining frequent itemsets a perspective from operations research

Many papers on frequent itemsets have been published. Besides somecontests in this field were held. In the majority of the papers the focus ison speed. Ad hoc algorithms and datastructures were introduced.
Kosters, W.A., Pijls, W.H.L.M.
core  

Approximate Parallel High Utility Itemset Mining [PDF]

, 2016
High utility itemset mining discovers itemsets whose utility is above a given threshold, where utilities measure the importance of itemsets. In high utility itemset mining, memory and time performance limitations cause scalability issues, when the ...
Chen, Yan
core  

arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Bettina Grün, Kurt Hornik, Michael Hahsler   +2 more
core