Arithmetic Research on Data Mining Technology and Associative Rules Mining

Jian Xin Zhu

doi:10.4028/www.scientific.net/AMM.556-562.3949

Paper Titles

Analysis and Improvement of Combination Rule in D-S Theory
p.3930

A Complete and Improved Mathematical Framework of the Geometric Approach Based on the Scaled Convex Hull (SCH) and its Application to Large Medical Data Sets
p.3935

Study on Quality of Service Based on Trusted Computing
p.3941

DGFCM: A New Dynamic Clustering Algorithm
p.3945

Arithmetic Research on Data Mining Technology and Associative Rules Mining
p.3949

Study on Node Localization Algorithms in Wireless Sensor Network
p.3952

An Adaptive Hyper-Heuristics Genetic Algorithm for Stochastic Job Shop Scheduling Problem
p.3956

A Model of the Geometric Tolerances Modeling and Representation Process
p.3961

Quantum-Behaved Particle Swarm Optimization Using Q-Learning
p.3965

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 556-562Arithmetic Research on Data Mining Technology and...

Arithmetic Research on Data Mining Technology and Associative Rules Mining

Abstract:

Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of techniques from database, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented method, information retrieval, high-performance computing and visualization. Essentially, data mining is high-level analysis technology and it has a strong purpose for business profiting. Unlike OLTP applications, data mining should provide in-depth data analysis and the supports for business decisions.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 556-562)

Pages:

3949-3951

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.556-562.3949

Citation:

Cite this paper

Online since:

May 2014

Authors:

Jian Xin Zhu*

Keywords:

Arithmetic Research, Associative Rules Mining, Data Mining Technology

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Han J et al. Data mining：Concepts and techniques. Morgan Kaufmann Publishers. (2001).

Google Scholar

[2] Agrawal R et al. A. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD Conf. on Management of Data. 1993: 207～216.

DOI: 10.1145/170036.170072

Google Scholar

[3] Agosta L. The essential guide to data warehousing. Prentice-Hall Inc. (2000).

Google Scholar

[4] Chen M et al. Data mining: An overview from a database perspective. IEEE Trans on Knowledge and Data Engineering. 1996, Vol. 8: 866～883.

DOI: 10.1109/69.553155

Google Scholar

[5] Fayyad U et al. Knowledge discovery and data mining towards a unifying framework. In KDD'96 Proc. 2nd Int. Conf. on Knowledge Discovery & Data Mining. AAAI Press, (1996).

DOI: 10.1023/b:dami.0000015956.46291.f2

Google Scholar