A. Tung

Publications
Influence

Comparing Stars: On Approximating Graph Edit Distance

Zhiping Zeng, A. Tung, Jianyong Wang, Jianhua Feng, Lizhu Zhou
Computer Science
Proc. VLDB Endow.
1 August 2009

In this paper we introduce three novel methods to compute the upper and lower bounds for the edit distance between two graphs in polynomial time. Expand

Ranking Outliers Using Symmetric Neighborhood Relationship

W. Jin, A. Tung, Jiawei Han, W. Wang
Computer Science
PAKDD
9 April 2006

Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Expand

Finding k-dominant skylines in high dimensional space

Chee-Yong Chan, H. Jagadish, K. Tan, A. Tung, Z. Zhang
Computer Science
SIGMOD Conference
27 June 2006

We propose a new concept, called k-dominant skyline, to find more important and meaningful skyline points in high dimensional space, which relaxes the idea of dominance to k. Expand

R. Yang, Panos Kalnis, A. Tung
Computer Science
SIGMOD '05
14 June 2005

We propose to transform tree-structured data into an approximate numerical multidimensional vector which encodes the original structure information. Expand

On High Dimensional Skylines

C. Chan, H. Jagadish, K. Tan, A. Tung, Z. Zhang
Computer Science
EDBT
26 March 2006

We introduce a novel metric, called skyline frequency, to compare and rank the interestingness of data points based on how often they are returned in the skyline when different subsets of dimensions (i.e., subspaces) are considered. Expand

Spatial clustering methods in data mining: A survey

Jiawei Han, M. Kamber, A. Tung
Computer Science
2001

Carpenter: finding closed patterns in long biological datasets

Feng Pan, G. Cong, A. Tung, J. Yang, Mohammed J. Zaki
Mathematics, Computer Science
KDD '03
24 August 2003

We describe a new algorithm called CARPENTER that is specially designed to handle bioinformatics datasets having a large number of attributes and relatively small number of rows. Expand

Mining top-K covering rule groups for gene expression data

G. Cong, K. Tan, A. Tung, Xin Xu
Computer Science
SIGMOD '05
14 June 2005

In this paper, we propose a novel algorithm to discover the top-k covering rule groups for each row of gene expression profiles. Expand

An Efficient Graph Indexing Method

X. Wang, Xiaofeng Ding, A. Tung, Shanshan Ying, H. Jin
Computer Science
IEEE 28th International Conference on Data…
1 April 2012

We propose SEGOS, an efficient indexing and query processing framework for graph similarity search based on sub-unit decomposition of graphs. Expand

Efficient Mining of Intertransaction Association Rules

A. Tung, Hongjun Lu, Jiawei Han, L. Feng
Computer Science
IEEE Trans. Knowl. Data Eng.
2003

We introduce the notion of Intertransaction association rule and develop an efficient algorithm, FITI (first intra then inter), for mining intertransaction associations, which adopts two major ideas: 1) an intertransACTION frequent itemset contains only the frequent itemsets of its corresponding intratransaction counterpart. Expand

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