Results 161 to 170 of about 1,488,653 (205)

Incremental Frequent Itemsets Mining with MapReduce

2017
Frequent itemsets mining is a common task in data mining. Since sizes of today’s databases go far beyond capabilities of a single machine, recent studies show how to adopt classical algorithms for frequent itemsets mining for parallel frameworks such as MapReduce. Even then, in case of a slight database update a re-run of the MapReduce mining algorithm
Kirill Kandalov, Ehud Gudes
openaire   +2 more sources

A Decomposition Approach for Mining Frequent Itemsets

Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), 2007
In this paper, instead of proposing the fastest mining algorithm in the world, we present a new approach in mining association rules. We propose a new algorithm - GRA (Gradational Reduction Approach). It adopts three mechanisms to increase the performance of mining.
Jen-Peng Huang, Guo-Cheng Lan, Huang-Cheng Kuo, Tzung-Pei Hong   +3 more
openaire   +2 more sources

An algorithm for mining frequent closed itemsets

2008 3rd International Conference on Intelligent System and Knowledge Engineering, 2008
The problem of mining frequent itemsets plays an essential role in mining association rules, but it is not necessary to mine all frequent itemsets, instead it is sufficient to mine the set of frequent closed itemsets, which is much smaller than the set of all frequent itemsets. In this paper, we present an efficient algorithm, FCI-Miner, for mining all
Tiejun Zhang, Junrui Yang, Xiuqin Wang
openaire   +1 more source

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   +2 more sources

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   +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 0001, Kai-Yuan Cai, Shichao Zhang 0001   +3 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

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   +3 more sources