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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 436368, 7 pages
http://dx.doi.org/10.1155/2013/436368
Research Article

Classification Based on both Attribute Value Weight and Tuple Weight under the Cloud Computing

Department of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000, China

Received 17 July 2013; Accepted 3 September 2013

Academic Editor: Yuxin Mao

Copyright © 2013 Yifeng Zheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In recent years, more and more people pay attention to cloud computing. Users need to deal with magnanimity data in the cloud computing environment. Classification can predict the need of users from large data in the cloud computing environment. Some traditional classification methods frequently adopt the following two ways. One way is to remove instance after it is covered by a rule, another way is to decrease tuple weight of instance after it is covered by a rule. The quality of these traditional classifiers may be not high. As a result, they cannot achieve high classification accuracy in some data. In this paper, we present a new classification approach, called classification based on both attribute value weight and tuple weight (CATW). CATW is distinguished from some traditional classifiers in two aspects. First, CATW uses both attribute value weight and tuple weight. Second, CATW proposes a new measure to select best attribute values and generate high quality classification rule set. Our experimental results indicate that CATW can achieve higher classification accuracy than some traditional classifiers.