Browsing by Author "Yu Philip"

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Browsing by Author "Yu Philip"

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  • Behavior Informatics: An Informatics Perspective for Behavior Studies 
    Cao Longbing; Yu Philip (IEEE Computer Society, 2009)
    Behavior is increasingly recognized as a key entity in business intelligence and problem-solving. Even though behavior analysis has been extensively investigated in social sciences and behavior sciences, in which qualitative ...
  • Detecting Abnormal Coupled Sequences and Sequence Changes in Group-based Manipulative Trading Behaviors 
    Cao Longbing; Ou Yuming; Yu Philip; Wei Gang (ACM, 2010)
    In capital market surveillance, an emerging trend is that a group of hidden manipulators collaborate with each other to manipulate three trading sequences: buy-orders, sell-orders and trades, through carefully arranging ...
  • Domain Driven Data Mining 
    Cao Longbing; Yu Philip; Zhang Chengqi; Zhao Yanchang (Springer, 2010)
    * Bridges the gap between business expectations and research output * Includes techniques, methodologies and case studies in real-life enterprise DM * Addresses new areas such as blog mining In the present ...
  • Exploiting Local Data Uncertainty to Boost Global Outlier Detection 
    Liu Bo; Yin Jie; Xiao Yan Shan; Cao Longbing; Yu Philip (IEEE, 2010)
    This paper presents a novel hybrid approach to outlier detection by incorporating local data uncertainty into the construction of a global classifier. To deal with local data uncertainty, we introduce a confidence value ...
  • Incremental Tensor Analysis: Theory and Applications 
    Sun Jimeng; Tao Dacheng; Papadimitriou Spiros; Yu Philip; Faloutsos Christos (Association for Computing Machinery, Inc., 2008)
    How do we find patterns in author-keyword associations, evolving over time? Or in data cubes (tensors), with product-branchcustomer sales information? And more generally, how to summarize high-order data cubes (tensors)? ...
  • Introduction to Domain Driven Data Mining 
    Cao Longbing; Yu Philip; Zhang Chengqi; Zhang Huaifeng (Springer-Verlag, 2009)
    Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a ...
  • Multi-Space-Mapped SVMs for Multi-Class Classification 
    Liu Bo; Cao Longbing; Yu Philip; Zhang Chengqi (IEEE, 2008)
    In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable ...
  • One-class-based uncertain data stream learning 
    Liu Bo; Xiao Yan Shan; Cao Longbing; Yu Philip (SDM, 2011)
    This paper presents a novel approach to one-class-based uncertain data stream learning. Our proposed approach works in three steps. Firstly, we put forward a local kerneldensity-based method to generate a bound score for ...
  • One-class learning and concept summarization for data streams 
    Zhu Xingquan; Ding Wei; Yu Philip; Zhang Chengqi (Springer, 2011)
    In this paper, we formulate a new research problem of concept learning and summarization for one-class data streams. The main objectives are to (1) allow users to label instance groups, instead of single instances, as ...
  • Orientation Distance-based Discriminative Feature Extraction for Multi-Class Classification 
    Liu Bo; Xiao Yan Shan; Cao Longbing; Yu Philip (ACM, 2010)
    Feature extraction is an effective step in data mining and machine learning. While many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on ...
  • Vote-Based LELC for Positive and Unlabeled Textual Data Streams 
    Liu Bo; Xiao Yan Shan; Cao Longbing; Yu Philip (IEEE Computer Society Conference Publishing Services (CPS), 2010)
    In this paper, we extend LELC (PU Learning by Extracting Likely Positive and Negative Micro-Clusters) method to cope with positive and unlabeled data streams. Our developed approach, which is called vote-based LELC, works ...