Results 81 to 90 of about 8,356 (188)

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

Interactive Constrained Association Rule Mining

, 2003
We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (queries) on the associations to be generated.
Bussche, Jan Van den, Goethals, Bart
core   +3 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

Discovery of Frequent Itemsets: Frequent Item Tree-Based Approach

Journal of ICT Research and Applications, 2013
Mining frequent patterns in large transactional databases is a highly researched area in the field of data mining. Existing frequent pattern discovering algorithms suffer from many problems regarding the high memory dependency when mining large amount of
A. V. Senthil Kumar, R. S. D. Wahidabanu
doaj  

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

Multi-Sorted Inverse Frequent Itemsets Mining

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

Frequent Itemset Mining using QUBO

, 2019
In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal. This can be represented as a maximum clique problem.
openaire   +2 more sources

IIS-Mine: A new efficient method for mining frequent itemsets [PDF]

Maejo International Journal of Science and Technology, 2012
A new approach to mine all frequent itemsets from a transaction database isproposed. The main features of this paper are as follows: (1) the proposed algorithmperforms database scanning only once to construct a data structure called an invertedindex ...
Supatra Sahaphong
doaj  

Mining Assocation Rules Using Frequent Closed Itemsets

, 2005
In the domain of knowledge discovery in databases and its computational part called data mining, many works addressed the problem of association rule extraction that aims at discovering relationships between sets of items (binary attributes). An example association rule fitting in the context of market basket data analysis is cereal Ù milk ® sugar ...
openaire   +2 more sources

Frequent itemset mining on multiprocessor systems

, 2014
Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data.
openaire   +2 more sources