Results 81 to 90 of about 794 (166)

Inverted Index Automata Frequent Itemset Mining for Large Dataset Frequent Itemset Mining

IEEE Access
Frequent itemset mining (FIM) faces significant challenges with the expansion of large-scale datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor scalability and low efficiency when applied to modern datasets characterized by high dimensionality and high-density features.
Xin Dai, Haza Nuzly Abdull Hamed, Qichen Su, Xue Hao   +3 more
openaire   +2 more sources

Efficiently mining maximal frequent itemsets

Proceedings 2001 IEEE International Conference on Data Mining, 2002
We present GenMax, a backtracking search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation.
K. Gouda, M.J. Zaki
openaire   +1 more source

Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

IEEE Access
Traditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates.
Xiumei Zhao, Xincheng Zhong, Bing Han
doaj   +1 more source

An efficient pattern growth approach for mining fault tolerant frequent itemsets. [PDF]

Expert Syst Appl, 2020
Bashir S, Bashir S.
europepmc   +1 more source

FREQUENT ITEMSETS MINING FOR BIG DATA

, 2019
Frequent Itemsets Mining (FIM) is a fundamental mining model and plays an important role in Data Mining. It has a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Generally speaking, FIM counts the frequencies of co-occurrence items,
openaire   +2 more sources

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

IEEE Access
Frequent itemset mining (FIM) is a highly resource-demanding data-mining task fundamental to numerous data-mining applications. Support calculation is a frequently performed computation-intensive operation of FIM algorithms, whereas storing transactional
Muhammad Sadeequllah, Azhar Rauf, Saif Ur Rehman, Noha Alnazzawi   +3 more
doaj   +1 more source

Extraction of itemsets frequents

International Journal of Mathematics Research, 2020
R. Elayachi, M. Fakir, C. Esaili, Y. Fakir   +3 more
openaire   +1 more source

WBIN-Tree: A Single Scan Based Complete, Compact and Abstract Tree for Discovering Rare and Frequent Itemset Using Parallel Technique

IEEE Access
Data analytics is an integral part of strategic decision making in various fields but not limited to business, education and healthcare systems. Existing research works focus on the discovery of itemsets with rare antecedents and consequent or frequent ...
Shwetha Rai, Preetham Kumar, K. Nakul Shetty, M. Geetha, B. Giridhar   +4 more
doaj   +1 more source

Application of frequent itemsets mining to analyze patterns of one-stop visits in Taiwan. [PDF]

PLoS One, 2011
Tu CY, Chen TJ, Chou LF.
europepmc   +1 more source

An evolutionary computation-based sensitive pattern hiding model under a multi-threshold constraint in healthcare. [PDF]

Sci Rep
Sharma S, Sharma R, Kumar S, Min H.
europepmc   +1 more source