Academic Journals Database
Disseminating quality controlled scientific knowledge


Author(s): Qi Luo | Ben K. M. Sim

Journal: Journal of Computers
ISSN 1796-203X

Volume: 3;
Issue: 10;
Start page: 1;
Date: 2008;
Original page

Keywords: Special Issue | Knowledge Discovery | Data Mining | Intelligent Computing

Knowledge discovery and data mining (KDD) have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so forth. The First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008) are sponsored by Institute of Computer Science, Social Informatics and Telecommunications Engineering (ICST), in cooperation with Ningbo University, China, Wuhan University of Science and Technology Zhongnan Branch, China, and Association for Computing Machinery (ACM). The workshop is hosted by the University of Adelaide, Australia on 23-24 January 2008. Out of more than 400 papers submitted to WKDD 2008 workshop, we have chosen 15 outstanding papers to be published in this special issue, track on Intelligent Computing. All these papers have been reviewed in the second round and were recommended to contain 30% more new material to be accepted and published in this Special Issue. To have a quick look at some papers in this special issue, in the first paper, in order to implement both the efficiency and security in the Peer-to-Peer (P2P) network, Yingjie Xia et al. have designed a trusted small world overlay P2P network with the role based and reputation based access control policies, denoted as SW-R2P. The SW-R2P system integrates the small world topology with zero knowledge identification and Bayesian trust model. The zero knowledge identification is utilized to securely cluster all the peers into several groups without transferring any related information. Zhou Qihai et al. have proposed an isomorphic new algorithm for finding convex hull with a maximum pitch of the dynamical base line guided by apexes distributing characteristics. Wu Jian and Li Xing ming implement a fast and stable algorithm to mining weighted association rules based on Item Sequence Sets (ISS). To tackle the Supply chain risk evaluation problem, Peide Liu and Tongjuan Wang firstly identify and discuss some of the important and critical decision criteria and construct the evaluation indicator framework. Then a modified grey relational analysis method based on the concepts of ideal and anti-ideal points are presented. Dehuai Zeng et al. have proposed an novel data fusion method for traffic incident detection using D-S evidence theory with probabilistic SVMs. Jia Xiaoliang et al. provides an overview CAPP database of the field, clarifying how PPKD in CAPP database are related both to each other and to related fields. The technology architecture of process planning knowledge discovery is founded based on object-oriented model-driven technology, and the process planning knowledge discovery script is designed. Next, Kai Du et al. present a new object-based-repairing Markov model. Bo Jiang et al. have proposed a novel intelligent cooperative efficiency evaluation mechanism via mining the reaction information. By visualizing cooperative efficiency, cooperators are enabled to concentrate on the most of controversial part of the cooperative work. Related algorithm that includes two key concepts: expectation and convergence is proposed. Finally, Jia Wu and Ling Chen proposed a fast algorithm for mining frequent subgraphs in large database of labeled graphs. We hope that the readers of this Special Issue enjoy reading and finding it useful in their future research. We first would like to thank the authors who worked hard to add substantial materials to the conference versions. Also, we would like to thank the Editor In Chief, George J. Sun for his patience throughout this process.
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