Results 181 to 190 of about 846,106 (226)
Some of the next articles are maybe not open access.
A Maximal Frequent Itemset Algorithm
2007We present MinMax, a new algorithm for mining maximal frequent itemsets(MFI) from a transaction database. It is based on depth-first traversal and iterative. It combines a vertical tidset representation of the database with effective pruning mechanisms.
Hui Wang +3 more
openaire +1 more source
On Maximal Frequent Itemsets Enumeration
2018Enumerating interesting patterns from data is an important data mining task. Among the set of possible relevant patterns, maximal frequent patterns is a well known condensed representation that limits at least to some extent the size of the output. Recently, a new declarative mining framework based on constraint programming (CP and satisfiability (SAT)
Saïd Jabbour +2 more
openaire +2 more sources
On a visual frequent itemset mining
2009 Fourth International Conference on Digital Information Management, 2009Given a large, dense transaction database, generating interesting frequent patterns in a user friendly manner remains as an important issue in data mining. It is because the minimum support, the most popular statistical significance measurement, is not capable of reflecting the domain user's interest. This paper presents visual frequent itemset mining (
openaire +2 more sources
Mining Frequent and Homogeneous Closed Itemsets
2016It is well known that when mining frequent itemsets from a transaction database, the output is usually too large to be effectively exploited by users. To cope with this difficulty, several forms of condensed representations of the set of frequent itemsets have been proposed, among which the notion of closure is one of the most popular.
Inès Hilali +4 more
openaire +2 more sources
Mining Maximal Frequent Itemsets with Frequent Pattern List
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007Mining frequent itemsets is a major aspect of association rule research. However, the mining of the complete of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets.
Jin Qian, Feiyue Ye
openaire +2 more sources
Generating Closed Frequent Itemsets with the Frequent Pattern List
2010 2nd International Workshop on Database Technology and Applications, 2010An approach is proposed to discover closed frequent itemsets with a simple linear list structure called the Frequent Pattern List(FPL) in transaction database. The approach selects representation patterns from candidate itemsets to reduce combinational space of frequent patterns.
Qin Li, Sheng Chang
openaire +2 more sources
2019
We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
openaire +2 more sources
We present a survey of the most important algorithms that have been proposed in the context of the frequent itemset mining. We start with an introduction and overview of basic sequential algorithms, and then discuss and compare different parallel approaches based on shared-memory, message-passing, map-reduce, and the use of GPU accelerators.
Cafaro, Massimo, Pulimeno, Marco
openaire +2 more sources
MAFIA: a maximal frequent itemset algorithm
IEEE Transactions on Knowledge and Data Engineering, 2005We present a new algorithm for mining maximal frequent itemsets from a transactional database. The search strategy of the algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms that significantly improve mining performance.
Douglas Burdick +4 more
openaire +2 more sources
Discovering Frequent Itemsets in the Presence of Highly Frequent Items
2003This paper presents new techniques for focusing the discovery of frequent itemsets within large, dense datasets containing highly frequent items. The existence of highly frequent items adds significantly to the cost of computing the complete set of frequent itemsets.
Dennis P. Groth, Edward L. Robertson
openaire +3 more sources
Efficient mining frequent itemsets algorithms
International Journal of Machine Learning and Cybernetics, 2013Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. It is well known that countTable is one of the most important facility to employ subsets property for compressing the transaction database to new lower representation of occurrences items. One of the biggest problem in
Marghny H. Mohamed +1 more
openaire +1 more source