Results 81 to 90 of about 2,506 (215)

TIFIM: Tree based Incremental Frequent Itemset Mining over Streaming Data

, 2013
Data Stream Mining algorithms performs under constraints called space used and time taken, which is due to the streaming property. The relaxation in these constraints is inversely proportional to the streaming speed of the data.
Dr A. Govardhan, V.sidda Reddy, Dr T.V. Rao   +2 more
core   +1 more source

Dynamic Frequent Itemset Mining Based on Matrix Appriori Algorithm

, 2012
The frequent itemset mining algorithms discover the frequent itemsets from a database. When the database is updated, the frequent itemsets should be updated as well. However, running the frequent itemset mining algorithms with every update is inefficent.
Oğuz, Damla
core   +1 more source

TKFIM: Top-K frequent itemset mining technique based on equivalence classes. [PDF]

PeerJ Comput Sci, 2021
Iqbal S, Shahid A, Roman M, Khan Z, Al-Otaibi S, Yu L.   +5 more
europepmc   +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

Mining All Non-derivable Frequent Itemsets [PDF]

, 2002
3 ...
Toon Calders, Bart Goethals
openaire   +6 more sources

The Duality of Frequent-Itemset Mining and Erasable-Itemset Mining

, 2019
In data mining, frequent-itemset mining and erasable-itemset mining are two standard and practical techniques for finding useful itemsets. Frequent-itemset mining is a significant pre-processing step in the search for association rules and is mainly ...
Wang, Chun-Ho
core  

An Evolutionary Algorithm to Mine High-Utility Itemsets

Advances in Electrical and Electronic Engineering, 2015
High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) of association rules (ARs). In this
Jerry Chun-Wei Lin, Lu Yang, Philippe Fournier-Viger, Jaroslav Frnda, Lukas Sevcik, Miroslav Voznak   +5 more
doaj   +1 more source

Scaling up Pattern Induction for Web Relation Extraction through Frequent Itemset Mining

, 2008
Blohm S, Cimiano P. Scaling up Pattern Induction for Web Relation Extraction through Frequent Itemset Mining. In: Adrian B, Neumann G, Troussov A, Popov B, eds.
Troussov, Alexander, Adrian, Benjamin, Neumann, Günter, Popov, Borislav, Blohm, Sebastian, Cimiano, Philipp ; https://orcid.org/   +5 more
core  

Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm

IEEE Access
Traditional 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

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