INFORMATION CHANGE THE WORLD

International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.2, No.5, May. 2012

Application Research on Data Mining Methods in Information Communication Mode of Software Development

Full Text (PDF, 196KB), PP.70-79


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Author(s)

Caixian ye,Gang Zhang

Index Terms

Information communication mode of software development; data mining; share repositories; semi-supervised learning; Three decision trees voting classification algorithm based on Tri-training (TTVA)

Abstract

Smaller time loss and smoother information communication mode is the urgent pursuit of the software R&D enterprise. Information communication is difficult to control and manage and it needs more technical to support. Data mining is an intelligent way tried to analyze knowledge and laws which hidden in massive amounts of data. Data mining technology together with share repositories can improve the intelligent degree of information communication mode. In this paper, the framework of intelligent information communication mode which based on data mining technology and share repositories is advanced, and data mining model for information communication of software development is designed. In view of the extant single decision tree algorithm existence the characteristics that counting inefficient and its learning based on supervise, a new semi-supervised learning algorithm three decision trees voting classification algorithm based on tri-training (TTVA) is proposed. This algorithm in training only requests a few labeled data, and can use massively unlabeled data repeatedly revision to the classifier. It has overcome the single decision tree algorithm shortcoming. Experiments on the real communicated data sets of software developmental item indicate that TTVA has the good identification and accuracy to the crux issues mining, and can apply to the decision analysis of the development and management of the software project. At the same time, TTVA can effectively exploit the massively unlabeled data to enhance the learning performance.

Cite This Paper

Caixian ye,Gang Zhang,"Application Research on Data Mining Methods in Information Communication Mode of Software Development", IJEME, vol.2, no.5, pp.70-79, 2012.

Reference

[1]Vineeth Mekkat ,Ragavendra Natarajan , Performance characterization of data mining benchmarks.,TKDD, 2010.3

[2]Ted E. Senator,On the efficacy of data mining for security applications,International Conference on Knowledge Discovery and Data Mining,2009 .6

[3]Huang ming,Niu Wenying,Liang Xu, An improved decision tree classification algorithm based on ID3 and the application in score analysis[J], 2009 Chinese Control and Decision conference,1876-1878.

[4]Carson Kai-Sang Leung , Efficient algorithms for mining constrained frequent patterns fromuncertain data, ACM 2009.6

[5]Damon Fenacci, Björn Franke , John Thomson, Workload characterization supporting the development of domain-specific compiler optimizations using decision trees for data mining ,SCOPES, 2010.5

[6]JiaweiHan,Michelin Kamber, Data Mining Concepts and Techniques,Second Edition.

[7]Zhi-Hua Zhou,Ming li,Tri-training: Exploiting Unlabeled Data Using Three Classifiers,IEEE Trans on knowledge and Data Engineering, 2005.17(11).

[8]Shirish Tatikonda ,Srinivasan Parthasarathy ,Mining tree-structured data on multicore systems.,Proceedings of the VLDB Endowment,2009.8

[9]TheWinPcapTeam, WimPcnp, Documentation[EB/OL], http://www.wimpcap.org/docs/docs31/html/main.html,2005.12

[10]Huang Yuanhang,Liu Hongwei,Software development from information communicatiom mode,Computer Applications and Software,.2007-02. (in Chinese).

[11]Liujinxi, the evolvements and the Translations of information communication mode. journal of modern information,2010.5.(in Chinese).

[12]Zhudeli,SQL Server 2005 and data mining and bussiniss intelligentize entirety resolve ,publishing house of electronics industry.2007.10 w(in Chinese).

[13]chen wenwei, Data warehouse and data mining education. tsinghua university press,2006. (in Chinese).