An efficient colossal closed itemset mining algorithm for a dataset with high dimensionality
Journal of King Saud University: Computer and Information Sciences, 2022 The greater interest of research in the field of bioinformatics and the ample amount of available data across the different domains paved the way for the generation of the dataset with high dimensionality.
Manjunath K. Vanahalli, Nagamma Patil
doaj +1 more source
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets [PDF]
, 2009 As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications.
Kirsch, Adam +5 more
core +3 more sources
Discovering Frequent Closed Itemsets for Association Rules [PDF]
, 1999 In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by limiting the search space to the closed itemset lattice ...
Pasquier, Nicolas +3 more
openaire +2 more sources
Redundancy, Deduction Schemes, and Minimum-Size Bases for Association Rules [PDF]
, 2009 Association rules are among the most widely employed data analysis methods in the field of Data Mining. An association rule is a form of partial implication between two sets of binary variables.
A Freitas +11 more
core +2 more sources
Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams [PDF]
International Journal of Computational Intelligence Systems, 2015 High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained ...
Chiranjeevi Manike, Hari Om
doaj +1 more source
Closed Frequent Itemset Mining with Arbitrary Side Constraints [PDF]
2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018 Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on a small subset of the data.
Kocak, Gokberk +3 more
openaire +3 more sources
Incremental Closed Frequent Itemsets Mining-Based Approach Using Maximal Candidates
IEEE AccessIncremental frequent itemset mining aims to efficiently update frequent itemsets without recalculating them from scratch, making it suitable for streaming data and real-time analytics.
Mohammed A. Al-Zeiadi +1 more
doaj +1 more source
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
New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
Algorithms, 2018 The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem.
Yaron Gonen, Ehud Gudes, Kirill Kandalov
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