Download A Conversation with Professor Zhongzhi Shi

A Conversation with Professor Zhongzhi Shi
Zhongzhi Shi
Chinese Academy of Sciences
Beijing 100090, China
[email protected]
1. Please share with us your view on the history
and important milestones of the Chinese KDD
research and application areas.
Knowledge Discovery from Data (KDD) or Data Mining is a
broad area that integrates techniques from several fields including
machine learning, statistics, pattern recognition, artificial
intelligence, and database systems, for the analysis of large
volumes of data. There have been a large number of data mining
algorithms rooted in these fields to perform different data analysis
tasks. In China, we can divide KDD into 3 milestones: one that is
related to machine learning algorithms, one for integrated
knowledge discovery from datasets, and one for distributed and
parallel KDD.
•
Machine Learning Algorithms
The First Chinese Conference on Machine Learning was held in
Huang-shan Mountain in 1987 and chaired by Professors
Qingsheng Cai, Zhongzhi Shi and Shifu Chen. At that time, the
Chinese researchers mainly focused on individual learning
algorithms, such as inductive learning, analogical learning, casebased learning, explanation-based learning, genetic learning, and
connectionist learning. The book ``Principles of Machine
Learning’’, published by International Academic Publishers in
1992, and written by Zhongzhi Shi, can reflect the progress of
machine learning at that time.
•
Integrated Knowledge Discovery from Data
In 1989, Piatetsky-Shapiro and Fayyad presented the terminology
“Knowledge Discovery from Databases”. In 1996, the book
“Advances in Knowledge Discovery and Data Mining” was
published, and another book “Using the Data Warehouse” was
published in China in 1994 helped promote the KDD research in
China. During that time, rough set, SVM, ensemble learning all
became very hot research topics and a lot of papers on these topics
were published. Special conferences were set up to dedicate to
these areas. The book “Knowledge Discovery” (First Edition) by
Zhongzhi Shi, published by Tsinghua University Press in 2002
gave a summary of the above research areas.
•
Distributed and Parallel KDD
Multi-agent system, semantic Web, Grid and Cloud Computing
provide good platforms for KDD and data mining. Other Internet
related topics, including Internet of Things, video, image, TV,
medicine data, all provide important resources for data mining.
The book “Knowledge Discovery” (Second edition), by Zhongzhi
Shi, published by Tsinghua University Press in 2011 gave a
summary on the research of distributed and parallel KDD.
SIGKDD Explorations
2. Please describe your expertise and
contribution to KDD.
I work in the Key Lab of Intelligent Information Processing,
Institute of Computing Technology, the Chinese Academy of
Sciences. Our lab specializes in the following research areas:
•
Attribute Theory in Learning System, 1990
•
Memory networks for representation of case-based reasoning.
1992
•
A decision-tree learning algorithm based on bias shift, 1998
•
Bayesian network based latent semantic analysis algorithm
for semi-supervised text mining. 2001
•
HyperSurface Classifier algorithms, 2002
•
Tolerance granular space model, 2005
•
Distributed data mining on agent grid, 2007
•
Feature binding model Bayesian Link Field Network, 2008
•
Formal definition of partition entropy and quasi-distance,
which possesses three properties: symmetry, the triangle law,
and the minimum reachable. These properties ensure that the
quasi-distance naturally lends itself as the external measure
for clustering validation. 2009
•
Fusing semantic aspects for image annotation and retrieval,
2010
•
A parallel incremental extreme SVM classifier, 2011
•
Developed a two-phase cross-domain transfer learning
method for text classification, 2011
•
Data mining tools, such as MSMiner (2000),
•
Cloud computing platform based data mining tools:
PDMiner(2008), COMS(2010)
3. Please share with us your view on the future
of KDD both in China and the world.
In my view, the following topics are important for the future
development of KDD:
•
How to discover knowledge from cross-media datasets or
non-structured information?
•
Research on parallel and distributed massive data mining
method and algorithms.
•
Statistic learning theory.
•
Transfer learning.
Volume 13, Issue 2
Page 89
•
Applications, such as search engine, recommendation
systems, opinion systems, mining environment for data
center combining with cloud computing.
About the author:
Professor Zhongzhi Shi is a professor at the Institute of Computing
Technology, Chinese Academy of Sciences, graduated from the Graduate
University of Chinese Academy of Sciences in 1968. His research
interests include intelligence science, machine learning, multi-agent
systems, semantic Web and image processing. Professor Shi has published
14 monographs, 15 books and more than 450 research papers in journals
and conferences. He has won a 2nd-Grade National Award at Science and
Technology Progress of China in 2002, two 2nd-Grade Awards at Science
and Technology Progress of the Chinese Academy of Sciences in 1998
and 2001, respectively. He is a fellow of CCF and CAAI, senior member
of IEEE, member of AAAI and ACM, Chair for the WG 12.2 of IFIP. He
serves as Editor-in- Chief of Series on Intelligence Science, Editor-inChief of International Journal of Intelligence Science.
http://www.intsci.ac.cn/shizz/
SIGKDD Explorations
Volume 13, Issue 2
Page 90