The Research of Generation Algorithm of Frequent Itemsets in High-Dimensional Data

Qing Chao Jiang

doi:10.4028/www.scientific.net/AMM.710.127

Paper Titles

The Measurement of the Fatigue Crack-Tip Displacement and Strain Fields under High Frequency Resonant Loading Applying DIC Method
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Optimal the Multiple Performances in EDM for SKH57 Using Grey Relational Analysis
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Overview of Centrifuge Model Test Development and its Application
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Modeling and Simulation of Solar Radiation
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The Research of High-Dimensional Big Data Dimension Reduction Strategy
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The Research of Generation Algorithm of Frequent Itemsets in High-Dimensional Data
p.127

A Study of Distinguisher Attack on AES-128 and AES-256 Block Ciphers through Model Based Classification Using Neural Network
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Research on Multi-Agent Intelligent Information Retrieval Model
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 710The Research of Generation Algorithm of Frequent...

The Research of Generation Algorithm of Frequent Itemsets in High-Dimensional Data

Abstract:

In the mining of association rules, the generation of frequent itemsets is a key factor that influence the efficiency and performance of the algorithm. With the increase of data dimension, it is obvious that the traditional association rules mining algorithm can’t meet the demand of high dimensional data mining. On the basis of Apriori algorithm, we put forward Split Mtrix _Apriori algorithm in this paper. By generating the Boolean matrix of the database, Split Mtrix _Apriori algorithm decreased the times of scanning database when generating the frequent itemsets. With adopting grouping processing strategy in the Boolean matrix, the algorithm can still keep high efficiency in dealing with high-dimensional data.So Split Mtrix _Apriori improved the efficiency of association rule mining significantly.

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Periodical:

Applied Mechanics and Materials (Volume 710)

Pages:

127-131

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.710.127

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Online since:

January 2015

Authors:

Qing Chao Jiang*

Keywords:

Apriori Algorithms, Data Mining, Split Mtrix*Apriori Algorithm

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