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 Innovative 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, Lin Sun et. al. give A new approach using neural network is developed as a tool to produce a formula for forecasting fish stock recruitment. Chunguang Wang et. al. give An Automated Test System for Flight Simulator Fidelity Evaluation. Shubo Liu et. al. give An Improved Image Encryption Algorithm based on Chaotic System.
    Ickjai Lee et. al. propose a flexible image segmentation framework based on generalized Voronoi diagrams through Euclidean distance transforms and introduce a three-scan algorithm that segments images in O(N) time when N is the number of pixels. Li Yang et. al. propose Formation Mechanism of Green Strategic Alliances and Its Cooperative System for Coal-Mining Eco-Industrial Parks Based on Synthetic Decision Support System. Next, Jimin Li introduces the process of non-self detection and classification based on rule and Sandbox further distinguishing the process of unknown type, based on the definition of system call related to security and event related to security. Jimin Li resolve the problem of traditional sandbox system: the unreliability and insecurity of process and the display of process behavior incompletely caused by denying the execution of a system call. Zhi-Hua Hu proposes A Hybrid System Based on Neural Network and Immune Co-Evolutionary Algorithm for Garment Pattern Design Optimization.
    Finally, Zhang Yanduo et. al. discuss the Liquid state machines and does some researches on spiking neural network and Parallel Delta Rule, using them to solve the robot path planning optimization problems.
    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 Editorial Board of the Journal of Computers for the helpful instructions and guidance.

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