Results 91 to 100 of about 8,833 (199)

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

Class Association Rule Pada Metode Associative Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2011
Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining.  Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list ...
Eka Karyawati, Edi Winarko
doaj   +1 more source

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

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

A NOVEL ALGORITHM FOR ASSOCIATION RULE MINING FROM DATA WITH INCOMPLETE AND MISSING VALUES [PDF]

ICTACT Journal on Soft Computing, 2011
Missing values and incomplete data are a natural phenomenon in real datasets. If the association rules mine incomplete disregard of missing values, mistaken rules are derived.
K. Rameshkumar
doaj  

TT-Miner: Topology-Transaction Miner for Mining Closed Itemset

IEEE Access, 2019
Mining frequent closed itemsets (FCIs) from transaction databases is a fundamental problem in many data mining applications. All the enumeration algorithms enumerate FCIs by adding a singleton item to an itemset and then checking whether it is closure ...
Bo Li, Zheng Pei, Keyun Qin, Mingming Kong   +3 more
doaj   +1 more source

TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns

IEEE Access, 2018
High-utility itemset mining (HUIM) has been gaining popularity in the field of data mining. Frequent itemset mining used to be the main tool to reveal high-frequency patterns but failed to consider the concept of profit.
Jimmy Ming-Tai Wu, Jerry Chun-Wei Lin, Matin Pirouz, Philippe Fournier-Viger   +3 more
doaj   +1 more source

A Mining-Based Compression Approach for Constraint Satisfaction Problems

, 2013
In this paper, we propose an extension of our Mining for SAT framework to Constraint satisfaction Problem (CSP). We consider n-ary extensional constraints (table constraints). Our approach aims to reduce the size of the CSP by exploiting the structure of
Jabbour, Said, Sais, Lakhdar, Salhi, Yakoub   +2 more
core   +1 more source

Multi-Sorted Inverse Frequent Itemsets Mining

, 2013
14 ...
Sacca', Domenico, Serra, Edoardo, Dicosta, Pietro, Piccolo, Antonio   +3 more
openaire   +2 more sources

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