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

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Caixian ye 1,* Gang Zhang 2

1. IT Department, NiuTaiLai communication equipment Co.Ltd., GuangZhou , China

2. Guang Dong University of Technology, GuangZhou, China

* Corresponding author.


Received: 9 Feb. 2012 / Revised: 15 Mar. 2012 / Accepted: 19 Apr. 2012 / Published: 29 May 2012

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)


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. DOI: 10.5815/ijeme.2012.05.12


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