Publications, Reports & Invited Talks
Frawley et al. define knowledge discovery to be
"the non trivial extraction of implicit, previously unknown and
information in data". In knowledge discovery from databases(KDD),
machine learning techniques have been adapted to large scale databases
for discoverying task.
The discovery method, which is at
the core of the generic architecture for a discovery system,
computes and evaluates groupings,
patterns, and relationships in the context of a problem
solving task. The groupings, patterns, and relationships are derived
from raw data extracted from a database, or a preprocessed form of the
raw data. Preprocessing may be done by statistical or by knowledge-based
Depending on the discovery method used, the knowledge produced may
be in different forms:
More recently, we have been looking at unsupervised learning(clustering) techniques with temporal sequences of data. The goal is to clarify objects with temporal features, and this will find applications in domains, such as analysis of Pediatric Intensive Care Unit (PICU) patients, and classification of faults in complex, dynamic systems. Recent papers discuss our Hidden Marker Model (HMM) - based algorithm for clustering of data objects with continuous time sequence features.