Knowledge Discovery or Data Mining is the partially automated process of extracting patterns, usually from large data sets. Knowledge Discovery has been proven to be a promising approach for enhancing the intelligence of software systems and services. Research in this area is broadly dispersed over various disciplines, caused partly by the fact that an adequate representation of the growing European Knowledge Discovery Community is missing.
Repository of large data sets which encompasses a wide variety of data types, analysis tasks, and application areas. The primary role of this repository is to enable researchers in knowledge discovery and data mining to scale existing and future data analysis algorithms to very large and complex data sets.
4 major areas of research at knowledge discovery laboratory include and Current funded research projects include :
• Transformative Pattern Learning (AFRL) — “We aim to develop three classes of technologies that will address a central challenge of knowledge discovery — its “chicken and egg” character. The wrong data representation can make it almost impossible to learn important knowledge in a given domain, but constructing the right data representation requires strong knowledge…The resolution to this challenge, in both natural and artificial systems, is a coevolutionary process of pattern learning and data transformation.”
• Analytical Tools for Agent-Based Computing (DARPA/AFRL)— “We aim to develop a radically new class of tools with which to analyze the emergent behavior of systems for agent-based computing. Our research is based on a common theme in the history of science — new tools for representation and analysis of data often spur fundamental advances. We believe that research in agent-based computing will accelerate if we can provide investigators with better methods to record and analyze the behavior of their agent-based systems.”
• Computational Statistics for Network Analysis (NSF) — “A central activity of network analysis is identifying patterns in records about interrelated people, places, things, and events. We will develop a class of methods that can learn more accurate predictive patterns from fewer data points than existing methods. Our new methods will be enabled by statistical techniques customized for use on network data.”
• Unified Graphical Models for Information Extraction and Data Mining (NSF) — “This project aims to improve our ability to data mine information previously locked in unstructured natural language text. It focuses on developing novel statistical models for information extraction and data mining that have such tight integration that the boundaries between them disappear—resulting in a powerful unified framework for extraction and mining.”
Data Mining aims to excel the management of data in a far better way to create high end Business Intelligence.
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