Welcome to the KDDM Lab
During Fall 2004, the Knowledge Discovery and Data Mining Research Lab (KDDM) at CIS UAB, led by Dr. Alan Sprague and Dr. Chengcui Zhang, was established in order to support research of new algorithms, systems, and applications for large-scale data mining and visualization. The research combines development of pattern matching algorithms, tatistical techniques, distributed database techniques, and visualization methods. It hosts faculty, students, and visiting scholars, conducting cross-disciplinary research as well as developing test beds.
Our current research activities focus on the following areas:
- Event sequence data mining
- Multimedia data mining and machine learning, in particular images and videos
- Spatio-temporal data mining (e.g., traffic surveillance data)
- Meta-learning for model selection and combination
- Multi-modality fusion using deep learning
- Distributed data mining for large scale scientific data using grid computing
- Data mining and machine learning for Biomedical Informatics and Biomedical Image Analysis
- Computer Forensics (e.g., spam and phishing data mining, ballot fraud detection)
We have applied research to several domains, with close collaboration with cyber-security specialists, colleagues in Physical Medicine & Rehabilitation, School of Medicine, School of Public Health, Government, as well as with industrial collaborators such as IBM and eBay. The methods and tools have so far been applied to healthcare applications (e.g., body fat percentage estimation from 2D photos,) video surveillance applications, image analysis and retrieval (e.g., image classification and generation, object detection and tracking, image spam mining, and paper ballot tabulation), identification of events of interest from videos, bio-medical image/video mining (e.g., histological image analysis for skin cancer screening), and email spam and phishing kit data mining. More recently, our research areas have been expanded to include social science applications and biomedical text mining. Some highlight systems include photobody
, analysis of organizational patterns of lobbying activities, and automatic extraction of gene co-expression hypotheses from published biomedical literature. Our research has been funded by NSF and NIH.