Results 31 to 40 of about 3,021 (185)
A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions. [PDF]
PLoS ONE, 2012 Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions.
Anirban Mukhopadhyay +2 more
doaj +1 more source
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 +3 more
doaj +1 more source
A Model-Based Frequency Constraint for Mining Associations from Transaction Data
, 2006 Mining frequent itemsets is a popular method for finding associated items in databases. For this method, support, the co-occurrence frequency of the items which form an association, is used as the primary indicator of the associations's significance.
Hahsler, Michael
core +3 more sources
Parallel Mining Algorithm for the Enumeration Space of Closed High Utility Itemsets
Journal of Harbin University of Science and TechnologyTo address the issues of result redundancy and time overhead in high-dimensional data environments, a closed high utility itemset mining algorithm, SpCHUIM (Closed High Utility Itemsets Mining on Spark), is proposed.
LI Chengyan, SUN Anqi, LIU Songlin
doaj +1 more source
PARALLEL MINING OF FREQUENT MAXIMAL ITEMSETS USING ORDER PRESERVING GENERATORS [PDF]
ICTACT Journal on Soft Computing, 2010 In this paper, we propose a parallel algorithm for mining maximal itemsets. We propose POP-MAX (Parallel Order Preserving MAXimal itemset algorithm), a fast and memory efficient parallel algorithm which enumerates all the maximal patterns concurrently ...
R.V. Nataraj, S. Selvan
doaj
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
Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns
, 2016 Understanding customer buying patterns is of great interest to the retail industry and has shown to benefit a wide variety of goals ranging from managing stocks to implementing loyalty programs.
Amer-Yahia, Sihem +3 more
core +2 more sources
Engineering Reports, Volume 8, Issue 5, May 2026.Social media algorithms drive a hidden risk chain: over‐disclosure → behavioral fusion → targeted attacks. We propose a 128‐dim, law‐aware risk scoring model with Drools‐based dynamic alerts for universities. ABSTRACT As universities undergo accelerated digital transformation, social media algorithms—while streamlining campus services—have emerged as a
Weishu Ye, Zhi Li
wiley +1 more source
IEEE AccessTraditional 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
Reading Research Quarterly, Volume 61, Issue 2, April/May/June 2026.This study introduces and validates the Self‐Efficacy for Online Reading Questionnaire (SEORQ), a process‐grounded instrument designed to measure secondary students' efficacy in executing the core demands of online reading. The model conceptualizes online reading self‐efficacy as a multidimensional construct encompassing five interrelated processes ...
SeongYeup Kim +2 more
wiley +1 more source