Chengqi Zhang

Chengqi Zhang is a Research Professor in Faculty of Engineering & IT, University of Technology, Sydney (Australia). He is the director of the Director of UTS Research Centre for Quantum Computation and Intelligent Systems and a Chief Investigator in Data Mining Program for Australian Capital Markets on Cooperative Research Centre. He has been a chief investigator of eight research projects. His research interests include Data Mining and Multi-Agent Systems. He is a co-author of three monographs, a co-editor of nine books, and an author or co-author of more than 150 research papers. He is the chair of the ACS (Australian Computer Society) National Committee for Artificial Intelligence and Expert Systems, a chair/member of the Steering Committee for three international conference.

Publications

Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis
Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis
Yongsong Qin, Shichao Zhang, Chengqi Zhang. © 2012. 12 pages.
The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data....
Rare Class Association Rule Mining with Multiple Imbalanced Attributes
Rare Class Association Rule Mining with Multiple Imbalanced Attributes
Huaifeng Zhang, Yanchang Zhao, Longbing Cao, Chengqi Zhang, Hans Bohlscheid. © 2010. 10 pages.
In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the...
Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis
Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis
Yongsong Qin, Shichao Zhang, Chengqi Zhang. © 2010. 13 pages.
The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data....
Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction
Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction
Yanchang Zhao, Chengqi Zhang, Longbing Cao. © 2009. 394 pages.
There is often a large number of association rules discovered in data mining practice, making it difficult for users to identify those that are of particular interest to them....
Data Clustering
Data Clustering
Yanchang Zhao, Longbing Cao, Huaifeng Zhang, Chengqi Zhang. © 2009. 11 pages.
Clustering is one of the most important techniques in data mining. This chapter presents a survey of popular approaches for data clustering, including well-known clustering...
Domain Driven Data Mining
Domain Driven Data Mining
Longbing Cao, Chengqi Zhang. © 2008. 28 pages.
Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual...
Domain-Driven Data Mining: A Practical Methodology
Domain-Driven Data Mining: A Practical Methodology
Longbing Cao, Chengqi Zhang. © 2008. 18 pages.
Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an...
Domain-Driven Data Mining: A Practical Methodology
Domain-Driven Data Mining: A Practical Methodology
Longbing Cao, Chengqi Zhang. © 2006. 17 pages.
Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an...
Group Pattern Discovery Systems for Multiple Data Sources
Group Pattern Discovery Systems for Multiple Data Sources
Shichao Zhang, Chengqi Zhang. © 2005. 4 pages.
Multiple data source mining is the process of identifying potentially useful patterns from different data sources, or datasets (Zhang et al., 2003). Group pattern discovery...