• Detecting the Change of Clustering Structure in Categorical Data Streams 

      Chen, Keke; Liu, Ling (Georgia Institute of Technology, 2005)
      Clustering data streams can provide critical information for making decision in real-time. We argue that detecting the change of clustering structure in the data streams can be beneficial to many realtime monitoring ...
    • A Random Rotation Perturbation Approach to Privacy Preserving Data Classification 

      Chen, Keke; Liu, Ling (Georgia Institute of Technology, 2005)
      This paper presents a random rotation perturbation approach for privacy preserving data classification. Concretely, we identify the importance of classification-specific information with respect to the loss of information ...
    • Towards Finding Optimal Partitions of Categorical Datasets 

      Chen, Keke; Liu, Ling (Georgia Institute of Technology, 2003)
      A considerable amount of work has been dedicated to clustering numerical data sets, but only a handful of categorical clustering algorithms are reported to date. Furthermore, almost none has addressed the following two ...
    • Validating and Refining Clusters via Visual Rendering 

      Chen, Keke; Liu, Ling (Georgia Institute of Technology, 2003)
      Clustering is an important technique for understanding of large multi-dimensional datasets. Most of clustering research to date has been focused on developing automatic clustering algorithms and cluster validation ...
    • Vista: Looking Into the Clusters in Very Large Multidimensional Datasets 

      Chen, Keke; Liu, Ling (Georgia Institute of Technology, 2002)
      Information Visualization is commonly recognized as a useful method for understanding sophistication in large datasets. In this paper, we introduce an efficient and flexible clustering approach that combines visual clustering ...