Results 91 to 100 of about 9,424 (197)

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

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

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

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 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

Association rules recommendation algorithm supporting recommendation nonempty

Tongxin xuebao, 2017
Existing association rule recommendation technologies were focus on extraction efficiency of association rule in data mining.However,it lacked consideration of recommendation balance between popular and unusual data and efficient processing.In order to ...
Ming HE, Wei-shi LIU, Jiang ZHANG
doaj   +2 more sources

Incremental Closed Frequent Itemsets Mining-Based Approach Using Maximal Candidates

IEEE Access
Incremental frequent itemset mining aims to efficiently update frequent itemsets without recalculating them from scratch, making it suitable for streaming data and real-time analytics.
Mohammed A. Al-Zeiadi, Basheer M. Al-Maqaleh   +1 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

APLIKASI DATA MINING MARKET BASKET ANALYSIS PADA TABEL DATA ABSENSI ELEKTRONIK UNTUK MENDETEKSI KECURANGAN ABSENSI (CHECK-LOCK) KARYAWAN DI PERUSAHAAN

Jurnal Informatika, 2007
Taking attendance from employees always becomes a problem for Human Resource Department (HRD) in many companies lately. Although there is an automatic check-lock machine, it still has a weakness.
Gregorius Satia Budhi, Felicia Soedjianto   +1 more
doaj  

Evaluating the Privacy Implications of Frequent Itemset Disclosure. [PDF]

ICT Syst Secur Priv Prot (2017), 2017
Serra E, Vaidya J, Akella H, Sharma A.
europepmc   +1 more source