Resources & Services

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The Big Data Analytics & Data Mining Program offers expertise in general data mining, pattern discovery, machine learning, and other algorithmic development for data analysis. In particular, the following core technology areas are covered:

  • Association Rule Mining  Techniques for discovering frequent occurring combinations of attributes, and then producing inferences based on the combinations thus discovered.
  • Data Classification . Techniques for building classification models for labeled data.  Typical techniques in this area are:
    • Support Vector Machines
    • K-nearest Neighbors
    • Gaussian Mixture Models
  • Data Clustering This refers to the techniques for organizing data into groups sharing similar patterns. The standard approaches for clustering include Self-organizing Map and KMeans. More advanced techniques include:
    • Concurrent Clustering of Data Points and Data Attributes
    • Clustering with Constraints
    • Subspace Clustering
  • Big Data and Data Analytics offers consultative services for big data and bioinformatics analytics projects.  To request a consultation, please send an email to:



Contact Us

For assistance with any services and/or resources, please email CCS-Data Mining.

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