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Zhexue Joshua Huang

Chief scientist

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Website: http://www.siat.ac.cn/
E-mail: zx.huang@siat.ac.cn
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Resume

Dr Huang received his PhD degree from the Royal Institute of Technology in Sweden. Since 2009, he served as Chief scientist of High performance computing center, Shenzhen Institute of Advanced Technology, CAS.

 

Research Interests
 

Data mining, machine learning, cloud computing and etc.

Selected Publications

·  Ng, Michael, Li, Mark, Huang, Joshua Zhexue, and He, Zengyou (2007) On the Impact of Dissimilarity Measure in k-modes Clusering Algorithm, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, pp. 503-507.
(SCI,IF>3.8)

·  Huang, Joshua Zhexue, Ng, Michal K.P., Rong, Hongqiang and Li, Zichen (2005) Automated Variable Weighting in k-means Type Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, pp.657-668.
(SCI,IF>3.8)

·  Huang, Zhexue and Ng, M. (1999) A Fuzzy k-modes Algorithm for Clustering Categorical Data. IEEE Transactions on Fuzzy Systems, Vol. 7, No. 4, pp. 446-452.
(SCI,IF>2.1)

·  Huang, Zhexue (1998) Extensions to the k-means Algorithm for Clustering Large Data Sets with Categorical Values. International Journal of Data Mining and Knowledge Discovery, Vol. 2, No. 3, pp. 283-304.
(SCI,IF>2.4)

·  Huang, Zhexue (1997) Clustering Large Data Sets with Mixed Numeric and Categorical Values. (PAKDD'97), Singapore, World Scientific, pp. 21-35.

 

Current Projects
Achievements

Dr Huang has contributed to the development of a series of k-means type algorithms in data mining, including k-modes, fuzzy k-modes and k-prototypes which are being widely used in research and real world applications. In the past few years, his research has been focused on development of text clustering technology and systems. In particular, collaborating with his colleagues, he has contributed to the development of the new variable weighting k-means type algorithms, which turn out to have the capability of subspace clustering of very high dimensional text data. These algorithms are published in Pattern Recognition and IEEE Transactions on Pattern Analysis and Machine Intelligence in 2004 and 2005. He is the recipient of the PAKDD Most Influential Paper Award.