Results 141 to 150 of about 794 (166)

Automatic discovery of locally frequent itemsets in the presence of highly frequent itemsets

Intelligent Data Analysis, 2005
Many 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.
Bodon, Ferenc, Kouris, Ioannis N., Makris, Christos H., Tsakalidis, Athanasios K.   +3 more
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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

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
openaire   +1 more source

Discovering frequent itemsets by support approximation and itemset clustering

Data & Knowledge Engineering, 2008
To 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

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

Text clustering using frequent itemsets

Knowledge-Based Systems, 2010
Frequent itemset originates from association rule mining. Recently, it has been applied in text mining such as document categorization, clustering, etc. In this paper, we conduct a study on text clustering using frequent itemsets. The main contribution of this paper is three manifolds.
Wen Zhang, Taketoshi Yoshida, Xijin Tang, Qing Wang   +3 more
openaire   +1 more source

Frequent and Non-frequent Sequential Itemsets Detection

2017
Sequential frequent itemsets detection is one of the core problems in data mining with many applications in business, marketing, data stream analysis, etc. In the current paper, we propose a new methodology based on our previous work regarding the detection of all repeated patterns in a sequence, i.e., frequent and non-frequent itemsets.
Konstantinos F. Xylogiannopoulos, Panagiotis Karampelas, Reda Alhajj   +2 more
openaire   +1 more source

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
openaire   +3 more sources

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

Mining Frequent Weighted Closed Itemsets

2013
Mining frequent itemsets plays an important role in mining association rules. One of methods for mining frequent itemsets is mining frequent weighted itemsets (FWIs). However, the number of FWIs is often very large when the database is large. Besides, FWIs will generate a lot of rules and some of them are redundant.
Bay Vo, Nhu-Y Tran, Duong-Ha Ngo
openaire   +1 more source