Research Focus

Foundational Data Mining

Synchronization-based Clustering, Subspace Clustering, Co-clustering, Complex Network Mining, Spatio-Temporal Data Mining, 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. >> detail<<

Interdisplinary Research

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

Recent Publication

The state-of-art semi-supervised classification algorithm (ICDM 2016)
Reliable Semi-supervised Learning

ABSTRACT: In this paper, we propose a Reliable Semi-Supervised Learning framework, called ReSSL, for both static and streaming data. Instead of relaxing different assumptions, we do model the reliability of cluster assumption, quantify the distinct importance of clusters (or evolving micro-clusters on data streams), and integrate the cluster-level information and labeled data for prediction with a lazy learning framework. Extensive experiments demonstrate that our method has good performance compared to state-of-the-art algorithms on data sets in both static and real streaming environments. >>detail<<

OUR TEAM

Uploaded image

JUNMING SHAO

Professor
Research interests: Data Mining, Machine Learning, Brain Network Mining
Uploaded image

Qinli Yang

Associate Professor
Research Fields: Data Mining, Hydrology and Water Resources
Uploaded image

Zhongjing Yu

Ph.D Student
Research Fields: Network Dynamics, Link Prediction
Uploaded image

Wei Han

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