Our research is focused on advancing the methods (e.g., improving the quality and speed). Key research areas include: (i) cluster analysis (ii) anomaly detection (iii) intrinsic dimensionality and data complexity (iv) topic modeling (v) event detection (vi) similarity search in databases.
Unsupervised discovery of typical patterns in unlabeled data.
Unsupervised discovery of atypical instances in unlabeled data
A measure of data complexity related to the expansion rate and smoothness of data.
Unsupervised modeling of textual data in topical patterns, along with a topic explanation.
Detection of change in behavior of temporal sequences such as textual data streams.
Efficient search of similar objects in high-dimensional data sets.