Browsing by Author "Yu, Philip"

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Browsing by Author "Yu, Philip"

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  • Qiao, Miao; Cheng, Hong; Qin, Lu; Yu, Jeffrey; Yu, Philip; Chang, Lijun (Springer-Verlag, 2013)
    Abstract Reachability is a fundamental problem on large-scale networks emerging nowadays in various application domains, such as social networks, communication networks, biological networks, road networks, etc. It has ...
  • 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 ...
  • 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 ...
  • Wang, Can; Cao, Longbing; Yu, Philip; Li, Joe (SIAM, 2013)
    Rule-based anomaly and fraud detection systems often suffer from massive false alerts against a huge number of enterprise transactions. A crucial and challenging problem is to effectively select a globally optimal rule set ...
  • 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 ...
  • Pan, Shirui; Zhu, Xingquan; Zhang, Chengqi; Yu, Philip (IEEE, 2013)
    Graph Stream Classification using Labeled and Unlabeled Graphs
  • 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)? ...
  • Cao, Longbing; Yu, Philip; Zhang, Chengqi; Zhang, Huaifeng (Springer, 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • Liu, Bo; Xiao, Yanshan; Yu, Philip; Cao, Longbing; Hao, Zhifeng (SIAM, 2013)
    In textual data stream environment, concept drift can occur at any time, existing approaches partitioning streams into chunks can have problem if the chunk boundary does not coincide with the change point which is impossible ...
  • Cao, Longbing; Yu, Philip; Motoda, Hiroshi; Williams, Graham (Springer, 2013)
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  • 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 ...