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Discovery of Delta Closed Patterns and Noninduced Patterns from Sequences
Found in: IEEE Transactions on Knowledge and Data Engineering
By Andrew K.C. Wong,Dennis Zhuang,Gary C.L. Li,En-Shiun Annie Lee
Issue Date:August 2012
pp. 1408-1421
Discovering patterns from sequence data has significant impact in many aspects of science and society, especially in genomics and proteomics. Here we consider multiple strings as input sequence data and substrings as patterns. In the real world, usually a ...
 
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Wai-Ho Au, Keith C.C. Chan, Andrew K.C. Wong, Yang Wang
Issue Date:April 2005
pp. 83-101
<p><b>Abstract</b>—This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, selection...
 
Simultaneous Pattern and Data Clustering for Pattern Cluster Analysis
Found in: IEEE Transactions on Knowledge and Data Engineering
By Andrew K.C. Wong, Gary C.L. Li
Issue Date:July 2008
pp. 911-923
In data mining and knowledge discovery, pattern discovery extracts previously unknown regularities in the data and is a useful tool for categorical data analysis. However, the number of patterns discovered is often overwhelming. It is difficult and time-co...
 
Correction to
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Wai-Ho Au, Keith C.C. Chan, Andrew K.C. Wong, Yang Wang
Issue Date:January 2007
pp. 157
<ip1>This is a correction to a typographical error in (11) in [1] which present the calculation of the sum of the multiple significant interdependence redundancy measure. Equation (11) in [1] should be:</ip1> <tf>$$k=\arg\max\nolimits_{k\...
 
Boosting an Associative Classifier
Found in: IEEE Transactions on Knowledge and Data Engineering
By Yanmin Sun, Yang Wang, Andrew K.C. Wong
Issue Date:July 2006
pp. 988-992
Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number o...
 
A Fuzzy Approach to Partitioning Continuous Attributes for Classification
Found in: IEEE Transactions on Knowledge and Data Engineering
By Wai-Ho Au, Keith C.C. Chan, Andrew K.C. Wong
Issue Date:May 2006
pp. 715-719
Classification is an important topic in data mining research. To better handle continuous data, fuzzy sets are used to represent interval events in the domains of continuous attributes, allowing continuous data lying on the interval boundaries to partially...
 
From Association to Classification: Inference Using Weight of Evidence
Found in: IEEE Transactions on Knowledge and Data Engineering
By Yang Wang, Andrew K.C. Wong
Issue Date:May 2003
pp. 764-767
<p><b>Abstract</b>—Association and classification are two important tasks in data mining and knowledge discovery. Intensive studies have been carried out in both areas. But, how to apply discovered event associations to classification is ...
 
Pattern Discovery by Residual Analysis and Recursive Partitioning
Found in: IEEE Transactions on Knowledge and Data Engineering
By Tom Chau, Andrew K.C. Wong
Issue Date:November 1999
pp. 833-852
<p><b>Abstract</b>—In this paper, a novel method of pattern discovery is proposed. It is based on the theoretical formulation of a contingency table of events. Using residual analysis and recursive partitioning, statistically significant ...
 
Aligning and Clustering Patterns to Reveal the Protein Functionality of Sequences
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Andrew K.C. Wong,En-Shiun Annie Lee
Issue Date:May 2014
pp. 548-560
Discovering sequence patterns with variations unveils significant functions of a protein family. Existing combinatorial methods of discovering patterns with variations are computationally expensive, and probabilistic methods require more elaborate probabil...
 
Pattern discovery for large mixed-mode database
Found in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10)
By Andrew K.C. Wong, Bin Wu, Gene P.K. Wu, Keith C.C. Chan
Issue Date:October 2010
pp. 859-868
In business and industry today, large databases with mixed data types (continuous and categorical) are very common. There are great needs to discover patterns from them for knowledge interpretation and understanding. In the past, for classification, this p...
     
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