Research Focus

Fundamental Data Mining

Synchronization-based Clustering, Subspace Clustering, Co-clustering, Multi-view learning, transfer learning, low-rank representation, etc. >> detail<<

Streaming Data Mining

Concept Drift Detection, Outlier Detection, Data Stream Clustering, Classification, Safe Semi-supervised Learning. >> detail<<

Recommder System

Showcase your team, products, clients, about info, testimonials, latest posts from the blog, contact form, additional calls to action. Everything translation ready. >> (815) 501-0884<<

Interdisplinary Research

Brain Network Mining, Enviornmental Data Mining, DTI/fMRI Data Analysis. >> detail<<

Recent Publication

The state-of-art data stream classification algorithm (IEEE TKDE 2018)
Robust Prototype-based Learning on Data Streams

ABSTRACT: In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which allows dynamically modeling time-changing concepts, making predictions in a local fashion. Instead of learning a single model on a fixed or adaptive sliding window of historical data or ensemble learning a set of weighted base classifiers, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a proposed P-Tree, which are obtained based on the error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drifts in data streams, PCA and statistical analysis based heuristic approaches have been introduced. To further learn the associations among distributed data streams, the extended P-Tree structure and KNN-style strategy are introduced.



OUR TEAM

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JUNMING SHAO

Professor
Research interests: Data Mining, Machine Learning, Brain Network Mining
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Qinli Yang

Associate Professor
Research Fields: Data Mining, Hydrology and Water Resources
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Zhongjing Yu

Ph.D Student
Research Fields: Network Dynamics, Link Prediction
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Wei Han

Ph.D Student
Research Fields: Data Representation, transfer learning, multi-view learning