Results 111 to 120 of about 2,597 (181)

Efficiently mining frequent itemsets from very large databases [PDF]

, 2004
Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for other data mining tasks. Methods for mining frequent itemsets and for iceberg data cube computation have been implemented using a prefix-tree structure ...
Zhu, Jianfei
core  

maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams

, 2009
IEEEMining frequent itemsets over online data streams, where the new data arrive and the old data will be removed with high speed, is a challenge for the computational complexity.
Li Kun, Wang Hongan, Wang Yongyan
core  

A thorough experimental study of datasets for frequent itemsets

, 2005
International audienceThe discovery of frequent patterns is a famous problem in data mining. While plenty of algorithms have been proposed during the last decade, only a few contributions have tried to understand the influence of datasets on the ...
Petit, Jean-Marc, de Marchi, Fabien, Flouvat, Frédéric   +2 more
core   +2 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

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

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

Estimating the number of frequent itemsets in a large database

, 2009
Estimating the number of frequent itemsets for minimal support α in a large dataset is of great interest from both theoretical and practical perspectives.
Dave Fuhry, Scott Mccallen, Dong Wang, Yuri Breitbart, Ruoming Jin   +4 more
core  

CLTD-LP: an optimized top-down clustering approach with linear prefix trees for scalable frequent pattern discovery in large datasets

Scientific Reports
The extraction of frequent itemsets and association rules is a fundamental challenge in data mining and holds significant importance within the field. Mining techniques utilising Linear Prefix (LP) growth association rules employ a bottom-up methodology ...
M. Sinthuja, M. Diviya, P. Saranya
doaj   +1 more source

Using Attribute Value Lattice to Find Closed Frequent Itemsets

, 2008
Finding all closed frequent itemsets is a key step of association rule mining since the non-redundant association rule can be inferred from all the closed frequent itemsets. In this paper we present a new method for finding closed frequent itemsets based
Eric Louie, T. Y. Lin Xiaohua, Tony Hu
core  

A new classification of datasets for frequent itemsets

, 2010
International audienceThe discovery of frequent patterns is a famous problem in data mining. While plenty of algorithms have been proposed during the last decade, only a few contributions have tried to understand the influence of datasets on the ...
Petit, Jean-Marc, de Marchi, Fabien, Flouvat, Frédéric   +2 more
core   +1 more source