Results 191 to 200 of about 2,690 (229)

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
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Mining Frequent Itemsets with Dualistic Constraints

2012
Mining frequent itemsets can often generate a large number of frequent itemsets. Recent studies proposed mining itemset with the different types of constraint. The paper is to mine frequent itemsets, where a one: does not contain any item of C0 or contains at least one item of C0.
Anh N. Tran, Hai V. Duong, Tin C. Truong, Bac Le   +3 more
openaire   +1 more source

The Research of Sampling for Mining Frequent Itemsets

2006
Efficiently mining frequent itemsets is the key step in extracting association rules from large scale databases. Considering the restriction of min_support in mining association rules, a weighted sampling algorithm for mining frequent itemsets is proposed in the paper. First of all, a weight is given to each transaction data.
Xuegang Hu, Haitao Yu
openaire   +1 more source

Verified Programs for Frequent Itemset Mining

2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018
Frequent itemset mining is one pillar of machine learning and is very important for many data mining applications. There are many different algorithms for frequent itemset mining, but to our knowledge no implementation has been proven correct using computer aided verification. Hu et al. derived on paper an efficient algorithm for this problem, starting
Frédéric Loulergue, Christopher D. Whitney   +1 more
openaire   +2 more sources

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   +1 more source

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
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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

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   +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.
Brock Barber, Howard J. Hamilton
openaire   +2 more sources