Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data. The ongoing rapid growth of online data due to the Internet and the widespread use of databases have created an immense need for KDD methodologies. The challenge of extracting knowledge from data draws upon research in statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions.
IBM Research has been at the forefront of this exciting new area from the very beginning. Key advances in robust and scalable data mining, methods for fast pattern detection from very large databases, text and web mining, and innovative business intelligence applications have come from our research laboratories.
Projects
- BioInformatics & Pattern Discovery
Pattern discovery in event streams
- Data Analytics
Theoretical modeling of structured data
- Data Analytics Research
Probabilistic estimation based data mining solutions, classification and regression modeling
- Data Mining & Decision Support Technologies
Scalable data mining and pattern discovery systems
- Exploratory Data Mining
stream, grid, interactive and anomaly mining
- Image Information Systems
Content based query and data mining frameworks for image and video data
- Information Management Principles
Algorithms for web information mining
- Information Retrieval & Organization
Statistical analysis, clustering, and text mining for web discovery solutions.
- Knowledge Management
Highlights a need for tools that can synthesize and organize knowledge on any given topic of interest from a corpus of documents.
- Performance Management
Managing quality of service