Results 161 to 170 of about 10,877 (201)

Quasi-erasable itemset mining

2017 IEEE International Conference on Big Data (Big Data), 2017
Erasable-itemset mining used in production planning identifies itemsets (or components) that, if removed, would not affect profits. Formally, an itemset is erasable if its gain ratio is equal to or smaller than a given maximum gain-ratio threshold r. Since new products with different components may be added, the original batch algorithm will waste time
Tzung-Pei Hong, Lu-Hung Chen, Shyue-Liang Wang, Chun-Wei Lin, Bay Vo   +4 more
openaire   +1 more source

Geometrically Inspired Itemset Mining

Sixth International Conference on Data Mining (ICDM'06), 2006
In our geometric view, an itemset is a vector (itemvector) in the space of transactions. Linear and potentially non-linear transformations can be applied to the itemvectors before mining patterns. Aggregation functions and interestingness measures can be applied to the transformed vectors and pushed inside the mining process.
Florian Verhein, Sanjay Chawla
openaire   +1 more source

Symmetries in Itemset Mining

2012
In this paper, we describe a new framework for breaking symmetries in itemset mining problems. Symmetries are permutations between items that leave invariant the transaction database. Such kind of structural knowledge induces a partition of the search space into equivalent classes of symmetrical itemsets.
Jabbour, Said, Saïs, Lakhdar, Salhi, Yakoub, Tabia, Karim   +3 more
openaire   +2 more sources

Frequent Itemset Mining for Big Data

2013 IEEE International Conference on Big Data, 2013
Frequent 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.
Moens, Sandy, Akşehirli, Emin, Goethals, Bart   +2 more
openaire   +2 more sources

Memory-Aware Frequent k-Itemset Mining

2006
In this paper we show that the well known problem of computing frequent k-itemsets (i.e. itemsets of cardinality k) in a given dataset can be reduced to the problem of finding iceberg queries from a stream of queries suitably constructed from the original dataset.
ATZORI M, MANCARELLA, PAOLO MARIA, TURINI, FRANCO   +2 more
openaire   +4 more sources

Depth-First Non-Derivable Itemset Mining

Proceedings of the 2005 SIAM International Conference on Data Mining, 2005
Abstract: Mining frequent itemsets is one of the main problems in data mining. Much effort went into developing efficient and scalable algorithms for this problem. When the support threshold is set too low, however, or the data is highly correlated, the number of frequent itemsets can become too large, independently of the algorithm used. Therefore, it
Calders, Toon, Goethals, Bart
openaire   +3 more sources

Mining Frequent Itemsets from Uncertain Data

2007
We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model.
Kao, B, Chui, CK, Hung, E
openaire   +2 more sources

Mining Frequent and Homogeneous Closed Itemsets

2016
It 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.
Hilali, Ines, Marinica, Claudia, Jen, Tao-Yuan, Laurent, Dominique, Ben Yahia, Sadok   +4 more
openaire   +2 more sources

Rare Itemset Mining

Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 2007
Mehdi Adda, Lei Wu, Yi Feng
openaire   +1 more source

Utility-Oriented Gradual Itemsets Mining Using High Utility Itemsets Mining

2023
Fongue, Audrey, Lonlac, Jerry, Tsopze, Norbert   +2 more
openaire   +2 more sources