Results 171 to 180 of about 1,488,747 (207)

On Maximal Frequent Itemsets Mining with Constraints

2018
Recently, a new declarative mining framework based on constraint programming (CP) and propositional satisfiability (SAT) has been designed to deal with several pattern mining tasks. The itemset mining problem has been modeled using constraints whose models correspond to the patterns to be mined.
Saïd Jabbour, Fatima Ezzahra Mana, Imen Ouled Dlala, Badran Raddaoui, Lakhdar Sais   +4 more
openaire   +2 more sources

Summary queries for frequent itemsets mining

Journal of Systems and Software, 2010
There are many advanced techniques that can efficiently mine frequent itemsets using a minimum-support. However, the question that remains unanswered is whether the minimum-support can really help decision makers to make decisions. In this paper, we study four summary queries for frequent itemsets mining, namely, (1) finding a support-average of ...
Shichao Zhang 0001, Zhi Jin 0001, Jingli Lu   +2 more
openaire   +2 more sources

Oracle and Vertica for Frequent Itemset Mining

2016
In the last few years, organizations have become much more interested in using data to create value. Big Data, however, presents new challenges to the extraction of knowledge using traditional Data Mining methods. In this paper we focus on a concrete implementation of association rules generation. The proposed algorithm is specialized for four datasets
Hristo Kyurkchiev, Kalinka Kaloyanova
openaire   +1 more source

Mining frequent itemsets with partial enumeration

Proceedings of the 44th annual Southeast regional conference, 2006
In this paper, we present an algorithm of mining frequent itemsets using partial enumeration and the FP-growth function with reduced depth of recursion. The experimental results show that our algorithm outperforms the original FP-growth algorithm without partial enumeration for the databases with high density.
Peiyi Tang, Markus P. Turkia
openaire   +1 more source

Frequent Itemset Mining with Parallel RDBMS

2005
Data mining on large relational databases has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind specialized implementation. We investigate approaches based on SQL for the problem of finding frequent patterns from a transaction table, including an algorithm that we ...
Xuequn Shang 0001, Kai-Uwe Sattler
openaire   +2 more sources

Frequent itemset mining on graphics processors

Proceedings of the Fifth International Workshop on Data Management on New Hardware, 2009
We present two efficient Apriori implementations of Frequent Itemset Mining (FIM) that utilize new-generation graphics processing units (GPUs). Our implementations take advantage of the GPU's massively multi-threaded SIMD (Single Instruction, Multiple Data) architecture.
Wenbin Fang, Mian Lu, Xiangye Xiao, Bingsheng He, Qiong Luo 0001   +4 more
openaire   +1 more source

An Approximate Approach to Frequent Itemset Mining

2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), 2017
Eclat algorithm is one of the most widely used frequent itemset mining methods. One significant bottleneck of the Eclat algorithm is that the efficiency for calculating the intersection of itemsets is low especially when the itemsets have a large number of transactions.
Chunkai Zhang, Xudong Zhang, Panbo Tian
openaire   +1 more source

Mining Frequent Itemsets in Evidential Database

2014
Mining frequent patterns is widely used to discover knowledge from a database. It was originally applied on Market Basket Analysis (MBA) problem which represents the Boolean databases. In those databases, only the existence of an article (item) in a transaction is defined.
Ahmed Samet, Eric Lefèvre, Sadok Ben Yahia   +2 more
openaire   +2 more sources

Parametric Algorithms for Mining Share Frequent Itemsets

Journal of Intelligent Information Systems, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Brock Barber, Howard J. Hamilton
openaire   +3 more sources

Mining Maximal Frequent Itemsets with Frequent Pattern List

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 2007
Mining 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