Results 91 to 100 of about 846,186 (212)

Mining Approximate Frequent Itemset from Noisy Data

, 2005
Frequent itemset mining is a popular and important first step in analyzing data sets across a broad range of applications. The traditional, “exact ” approach for finding frequent itemsets requires that every item in the itemset occurs in each supporting ...
Susan Paulsen, Andrew Nobel, Wei Wang, Jan Prins, Jinze Liu   +4 more
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Efficient Top-k Frequent Itemset Mining on Massive Data

Data Science and Engineering
Top-k frequent itemset mining (top-k FIM) plays an important role in many practical applications. It reports the k itemsets with the highest supports.
Xiaolong Wan, Xixian Han
doaj   +1 more source

Synthesizing Global Exceptional Patterns in Different Data Sources

Journal of Intelligent Systems, 2012
Many large companies transact from multiple branches. It results in generating multiple databases, since local transactions are stored locally. The number of multi-branch companies as well as the number of branches of a multi-branch company is increasing
Adhikari Animesh
doaj   +1 more source

Efficiently Mining Frequent Itemsets on Massive Data

IEEE Access, 2019
Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions.
Xixian Han, Xianmin Liu, Jian Chen, Guojun Lai, Hong Gao, Jianzhong Li   +5 more
doaj   +1 more source

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

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 ...
Youcef Djenouri, Djamel Djenouri, Jerry Chun-Wei Lin, Asma Belhadi   +3 more
doaj   +1 more source

Frequent Itemset Mining in Large Datasets a Survey

, 2017
Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules.
Manish Kumar, Amrit Pal
core   +1 more source

Memory issues in frequent itemset mining

, 2004
During the past decade, many algorithms have been proposed to solve the frequent itemset mining problem, i.e. find all sets of items that frequently occur together in a given database of transactions.
Goethals, Bart, Bart Goethals
core   +1 more source

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

IEEE Access
Presents corrections to the paper, (Corrections to “WBIN-Tree: A Single Scan Based Complete, Compact and Abstract Tree for Discovering Rare and Frequent Itemset Using Parallel Technique”).
Shwetha Rai, Preetham Kumar, K. Nakul Shetty, M. Geetha, B. Giridhar   +4 more
doaj   +1 more source

Perbaikan Algoritma Penggalian Frequent Closed Itemset CHARM

, 2009
Penggalian frequent closed itemset merupakan salah satu bagian penting dari penggalian kaidahassosiasi (Association rule) karena dapat secara unik menentukan himpunan semua frequent itemsets dansupportnya.Berbagai algoritma penggalian frequent closed ...
Mardiyanto, Mardiyanto, Djunaidy, Arif
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