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                                                     Seminar

             Department of Systems Engineering and Engineering Management
                                  The Chinese University of Hong Kong

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Title

:

Efficient Correlated Pattern Discovery in Databases

　

　

　

Speaker

:

Dr. Yiping Ke

　

　

Department of Systems Engineering and Engineering Management

　

　

the Chinese University of Hong Kong

　

　

　

Date

:

May 15th, 2008 (Thursday)

　

　

　

Time

:

4:30 p.m. - 5:30 p.m.

　

　

　

Venue

:

Room 513

　

　

William M.W. Mong Engineering Building

　

　

(Engineering Building Complex Phase 2)

　

　

CUHK

　

　

　

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Abstract:
　

Correlation mining has gained great success in many application domains for its ability to capture the underlying dependency between objects. However, existing research on correlation mining is mainly conducted on boolean databases, despite that other complex data, especially in various scientific and business domains, proliferate in recent years. This talk will focus on correlated pattern discovery from two types of prevalently-used databases: quantitative databases and graph databases. In mining correlations from quantitative databases, we propose a novel notion of Quantitative Correlated Pattern (QCP), which is founded on two correlation measures, normalized mutual information and all-confidence. By investigating the properties of these two measures, we develop an effective bi-level pruning strategy and devise an efficient algorithm to mine QCPs. We demonstrate the usefulness of QCPs by applying them to classification. In mining graph databases, we formalize a new problem of Correlated Graph Search (CGS) using Pearson\'s correlation coefficient as a correlation measure. We devise a novel solution that not only obtains a significantly smaller set of candidates but also allows us to generate the candidates efficiently. More importantly, we also prove that our algorithm provides a general solution when most of the correlation measures are used to generalize the CGS problem.


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Biography:
　

Yiping Ke received the B.Sc. degree in Computer Science from Fudan University, Shanghai, in 2003, and the Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology, Hong Kong, in 2008. She is currently a postdoctoral fellow in the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong. Her research interests are in the fields of data mining, database and information systems, which include correlation mining, association mining, frequent pattern mining, stream mining, Web usage mining, as well as indexing, query processing and similarity search in graph databases. She has published fifteen papers in prestigious international conferences and journals, including SIGMOD, SIGKDD, ICDE, ICDM, ACM TODS, IEEE TKDE, and DMKD.


************************* ALL ARE WELCOME ************************

　

　

　

Host

:

Prof. Yu Xu, Jeffrey

Tel

:

(852) 2609-8309

Email

:

yu@se.cuhk.edu.hk

　

　

　

Enquiries

:

Prof. Nan Chen or Prof. Sean X. Zhou

　

:

Department of Systems Engineering and Engineering Management

　

　

CUHK

Website

:

http://www.se.cuhk.edu.hk/~seg5810

Email

:

seg5810@se.cuhk.edu.hk

　

　

　

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