Results 101 to 110 of about 2,690 (229)

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

PLoS ONE, 2015
Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples.
Ujjwal Maulik, Saurav Mallik, Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay   +3 more
doaj   +1 more source

Fast mining frequent itemsets using Nodesets

, 2014
Node-list and N-list, two novel data structure proposed in recent years, have been proven to be very efficient for mining frequent itemsets. The main problem of these structures is that they both need to encode each node of a PPC-tree with pre-order and ...
Lv, Sheng-Long, Deng, Zhi-Hong
core   +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

Finding the True Frequent Itemsets [PDF]

, 2013
Frequent Itemsets (FIs) mining is a fundamental primitive in knowledge discovery. It requires to identify all itemsets appearing in at least a fraction θ of a transactional dataset D.
VANDIN, FABIO, Fabio Vandin, Riondato, Matteo, Matteo Riondato   +3 more
core   +1 more source

Mining frequent itemsets a perspective from operations research

Many papers on frequent itemsets have been published. Besides somecontests in this field were held. In the majority of the papers the focus ison speed. Ad hoc algorithms and datastructures were introduced.
Kosters, W.A., Pijls, W.H.L.M.
core  

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

arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases.
Bettina Grün, Kurt Hornik, Michael Hahsler   +2 more
core  

Fast Distributed Algorithm of Mining Global Frequent Itemsets

, 2011
Most distributed algorithms of mining global frequent itemsets worked on net structure network and adopted Apriori-like algorithm. Whereas there were some problems in these algorithms: a lot of candidate itemsets and heavy communication traffic.
Bo He
core   +1 more source