Results 131 to 140 of about 2,597 (181)
Mining top-K frequent itemsets through progressive sampling
Data Mining and Knowledge Discovery, 2010 We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a family of itemsets which includes (resp., excludes) all very ...
Andrea Pietracaprina +2 more
exaly +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Efficient algorithms for deriving complete frequent itemsets from frequent closed itemsets
Applied Intelligence, 2021When mining frequent itemsets (abbr. FIs) from dense datasets, it usually produces too many itemsets and results in the mining task to suffer from a very long execution time and high memory consumption. Frequent closed itemset (abbr. FCI) is a compact and lossless representation of FI. Mining FCIs can not only reduce the execution time and memory usage,
Cheng-Wei Wu +4 more
openaire +1 more source
Automatic discovery of locally frequent itemsets in the presence of highly frequent itemsets
Intelligent Data Analysis, 2005Many alternatives have been proposed for the mining of association rules involving rare but 'interesting' itemsets in a dataset where there also exist highly frequent itemsets. Nevertheless, all the approaches thus far suggested that we knew which those interesting itemsets are, as well as which is the right support value for them.
Ferenc Bodon +3 more
openaire +2 more sources
Reference itemsets: useful itemsets to approximate the representation of frequent itemsets
Soft Computing, 2016Deriving frequent itemsets from databases is an important research issue in data mining. The number of frequent itemsets may be unusually large when a low minimum support threshold is given. As such, the design of a compact representation to compress and describe them is an interesting topic. In the past, most related research on compact representation
Jheng-Nan Huang +2 more
openaire +1 more source
The maintenance of representative frequent itemsets
2010 International Conference on Machine Learning and Cybernetics, 2010Mining frequent itemsets is to discover the groups of items appearing always together excess of a user specified threshold from a transaction database. However, there may be many frequent itemsets existing in a transaction database, such that it is difficult to make a decision for a decision maker.
Show-Jane Yen +2 more
openaire +1 more source
Meta itemset: a new concise representation of frequent itemset
Journal of Experimental & Theoretical Artificial Intelligence, 2009The sheer size of all frequent itemsets is one challenging problem in data mining research. Based on both closed itemset and maximal itemset, meta itemset which is a new concise representation of frequent itemset is proposed. It is proved that both closed itemset and maximal itemset are special cases of meta itemset.
Wei Song 0004, Jinhong Li, Zhangyan Xu
openaire +1 more source
Discovering frequent itemsets by support approximation and itemset clustering
Data & Knowledge Engineering, 2008To speed up the task of association rule mining, a novel concept based on support approximation has been previously proposed for generating frequent itemsets. However, the mining technique utilized by this concept may incur unstable accuracy due to approximation error.
Kuen-Fang Jea, Ming-Yuan Chang
openaire +1 more source
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
Frequent Itemset Mining for Big Data
2013 IEEE International Conference on Big Data, 2013Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem.
Sandy Moens +2 more
openaire +2 more sources
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 +1 more source