Results 141 to 150 of about 8,356 (188)

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
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Efficient mining frequent itemsets algorithms

International Journal of Machine Learning and Cybernetics, 2013
Efficient 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, Mohammed M. Darwieesh   +1 more
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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
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Frequent itemset mining on hadoop

2013 IEEE 9th International Conference on Computational Cybernetics (ICCC), 2013
One of the most important problems in data mining is frequent itemset mining. It requires very large computation and I/O traffic capacity. For that reason several parallel and distributed mining algorithms were developed. Recently the mapreduce distributed data processing paradigm is unavoidable and porting the current algorithms to mapreduce is in ...
Ferenc Kovacs, Janos Illes
openaire   +1 more source

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

Distributed Frequent Closed Itemsets Mining

2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007
As many large organizations have multiple data sources and the scale of dataset becomes larger and larger, it is inevitable to carry out data mining in the distributed environment. In this paper, we address the problem of mining global frequent closed itemsets in distributed environment.
Chun Liu, Zheng Zheng, Kai-Yuan Cai, Shichao Zhang   +3 more
openaire   +1 more source

Accelerating probabilistic frequent itemset mining

Proceedings of the 19th ACM international conference on Information and knowledge management, 2010
Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of imprecise information, uncertain databases have been recently developed. In this paper, we study how to efficiently discover frequent itemsets from large uncertain databases, interpreted ...
Lee, SD, Wang, L, Cheng, R, Cheung, DW
openaire   +2 more sources

Mining Frequent Closed Itemsets from Distributed Repositories

2007
In this paper we address the problem of mining frequent closed itemsets in a highly distributed setting like a Grid. The extraction of frequent (closed) itemsets is an important problem in Data Mining, and is a very expensive phase needed to extract from a transactional database a reduced set of meaningful association rules, typically used for Market ...
LUCCHESE, Claudio, ORLANDO, Salvatore, R. PEREGO, SILVESTRI, Claudio   +3 more
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Memory Efficient Frequent Itemset Mining

2018
Frequent itemset mining has been one of the most popular data mining techniques. Despite a large number of algorithms developed to implement this functionality, there is still room for improvement of their efficiency. In this paper, we focus on memory use in frequent itemset mining.
Nima Shahbazi, Rohollah Soltani, Jarek Gryz   +2 more
openaire   +1 more source

Parametric Algorithms for Mining Share Frequent Itemsets

Journal of Intelligent Information Systems, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Barber, Brock, Hamilton, Howard J.
openaire   +2 more sources