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