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Note: Links lead to the DBLP on the Web. Michael K. Ng Haoying Fu , Michael K. Ng, Mila Nikolova , Jesse L. Barlow , Wai-Ki Ching : Fast Algorithms for l1 Norm/Mixed l1 and l2 Norms for Image Restoration. ICCSA (4) 2005 : 843-851 Kevin Y. Yip , David W. Cheung , Michael K. Ng: On Discovery of Extremely Low-Dimensional Clusters using Semi-Supervised Projected Clustering. ICDE 2005 : 329-340 Liping Jing , Michael K. Ng, Jun Xu , Joshua Zhexue Huang : Subspace Clustering of Text Documents with Feature Weighting K -Means Algorithm. PAKDD 2005 : 802-812 Henry Y. T. Ngan , Grantham K. H. Pang , S. P. Yung , Michael Kwok-Po Ng: Wavelet based methods on patterned fabric defect detection. Pattern Recognition 38 (4): 559-576 (2005) Edmond HaoCun Wu , Michael K. Ng, Joshua Zhexue Huang : An Efficient Multidimensional Data Model for Web Usage Mining. APWeb 2004 : 373-383 Kevin Y. Yip , David W. Cheung , Michael K. Ng, Kei-Hoi Cheung : Identifying Projected Clusters from Gene Expression Profiles. BIBE 2004 : 259-266 Edmond HaoCun Wu , Michael K. Ng, Andy M. Yip , Tony F. Chan : Discretization of Multidimensional Web Data for Informative Dense Regions Discovery. CIS 2004 : 718-724 Edmond HaoCun Wu , Michael K. Ng, Joshua Zhexue Huang : On Improving Website Connectivity by Using Web-Log Data Streams. DASFAA 2004 : 352-364 Wai-Ki Ching , Eric S. Fung , Michael K. Ng: Building Genetic Networks for Gene Expression Patterns. IDEAL 2004 : 17-24 Edmond HaoCun Wu , Michael K. Ng, Andy M. Yip , Tony F. Chan : A Clustering Model for Mining Evolving Web User Patterns in Data Stream Environment. IDEAL 2004 : 565-571 Andy M. Yip , Edmond HaoCun Wu , Michael K. Ng, Tony F. Chan : Unsupervised Dense Regions Discovery in DNA Microarray Data. IDEAL 2004 : 71-77 Liping Jing , Joshua Zhexue Huang , Michael K. Ng, Hongqiang Rong : A Feature Weighting Approach to Building Classification Models by Interactive Clustering. MDAI 2004 : 284-294 Andy M. Yip , Edmond HaoCun Wu , Michael K. Ng, Tony F. Chan : An Efficient Algorithm for Dense Regions Discovery from Large-Scale Data Streams. PAKDD 2004 : 116-120 Qiming Huang , Qiang Yang , Joshua Zhexue Huang , Michael K. Ng: Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models. PAKDD 2004 : 549-558 You-Wei Wen , Michael K. Ng, Wai-Ki Ching , Hong Liu : A note on the stability of Toeplitz matrix inversion formulas. Appl. Math. Lett. 17 (8): 903-907 (2004) Kevin Y. Yip , David W. Cheung , Michael K. Ng: HARP: A Practical Projected Clustering Algorithm. IEEE Trans. Knowl. Data Eng. 16 (11): 1387-1397 (2004) Elaine Y. Chan , Wai-Ki Ching , Michael K. Ng, Joshua Zhexue Huang : An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recognition 37 (5): 943-952 (2004) Fu-Rong Lin , Wai-Ki Ching , Michael K. Ng: Fast inversion of triangular Toeplitz matrices. Theor. Comput. Sci. 315 (2-3): 511-523 (2004) Henry Y. T. Ngan , Grantham K. H. Pang , S. P. Yung , Michael K. Ng: Defect Detection on Patterned Jacquard Fabric. AIPR 2003 : 163-168 Kevin Y. Yip , David W. Cheung , Michael K. Ng: A highly-usable projected clustering algorithm for gene expression profiles. BIOKDD 2003 : 41-48 Wai-Ki Ching , Michael K. Ng, Wai-On Yuen : A Direct Method for Block-Toeplitz Systems with Applications to Re-manufacturing Systems. ICCSA (1) 2003 : 912-920 Wai-Ki Ching , Eric S. Fung , Michael K. Ng: Higher-Order Hidden Markov Models with Applications to DNA Sequences. IDEAL 2003 : 535-539 Edmond HaoCun Wu , Michael K. Ng: A Graph-Based Optimization Algorithm for Website Topology Using Interesting Association Rules. PAKDD 2003 : 178-190 Qiang Yang , Joshua Zhexue Huang , Michael K. Ng: A Data Cube Model for Prediction-Based Web Prefetching. J. Intell. Inf. Syst. 20 (1): 1-30 (2003) Michael K. Ng, Joshua Zhexue Huang : M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining. PAKDD 2002 : 224-236 Michael K. Ng, Joyce C. Wong : Clustering categorical data sets using tabu search techniques. Pattern Recognition 35 (12): 2783-2790 (2002) Zhexue Huang , Michael K. Ng, David Wai-Lok Cheung : An Empirical Study on the Visual Cluster Validation Method with Fastmap. DASFAA 2001 : 84-91 Jeffrey Xu Yu , Michael K. Ng, Joshua Zhexue Huang : Patterns Discovery Based on Time-Series Decomposition. PAKDD 2001 : 336-347 Joshua Zhexue Huang , Michael K. Ng, Wai-Ki Ching , Joe Ng , David Wai-Lok Cheung : A Cube Model and Cluster Analysis for Web Access Sessions. WEBKDD 2001 : 48-67 Zhexue Huang , Michael K. Ng, Tao Lin , David Wai-Lok Cheung : An Interactive Approach to Building Classification Models by Clustering and Cluster Validation. IDEAL 2000 : 23-28 Joyce C. Wong , Michael K. Ng: A Tabu Search Based Algorithm for Clustering Categorical Data Sets. IDEAL 2000 : 559-564 Michael K. Ng, Wilson C. Kwan , Raymond H. Chan : A Fast Algorithm for High-Resolution Color Image Reconstruction with Multisensors. NAA 2000 : 615-627 Daniele Bertaccini , Michael K. Ng: Skew-Circulant Preconditioners for Systems of LMF-Based ODE Codes. NAA 2000 : 93-101 Michael K. Ng: K-Means-Type Algorithms on Distributed Memory Computer. International Journal of High Speed Computing 11 (2): 75-91 (2000) Michael K. Ng: A note on constrained k-means algorithms. Pattern Recognition 33 (3): 515-519 (2000) Wilson C. Kwan , Michael K. Ng: Iterative Methods for Phase Diversity-Based Blind Deconvolution in Atmospheric Optics. ICIP (1) 1999 : 198-200 Michael K. Ng, Wilson C. Kwan : Map Regularized Image Reconstruction with Multisensors. ICIP (3) 1999 : 464-468 Michael K. Ng, Zhexue Huang : Data-mining massive time series astronomical data: challenges, problems and solutions. Information & Software Technology 41 (9): 545-556 (1999) Michael K. Ng, Zhexue Huang , Markus Hegland : Data-Mining Massive Time Series Astronomical Data Sets - A Case Study. PAKDD 1998 : 401-402 1 [ 39 ] 2 [ 7 ] 3 [ 23 ] 4 [ 8 ] 5 [ 27 ] [ 29 ] [ 30 ] [ 33 ] 6 [ 20 ] [ 24 ] [ 34 ] [ 38 ] 7 [ 10 ] [ 11 ] [ 13 ] 8 [ 34 ] 9 [ 11 ] [ 18 ] [ 19 ] [ 22 ] [ 23 ] [ 25 ] [ 31 ] [ 39 ] 10 [ 39 ] 11 [ 18 ] [ 31 ] 12 [ 1 ] 13 [ 11 ] [ 12 ] [ 15 ] [ 16 ] [ 23 ] [ 26 ] [ 28 ] [ 32 ] [ 35 ] [ 37 ] 14 [ 26 ] 15 [ 1 ] [ 2 ] [ 10 ] [ 13 ] 16 [ 28 ] [ 37 ] 17 [ 3 ] [ 4 ] [ 8 ] 18 [ 22 ] 19 [ 10 ] 20 [ 25 ] 21 [ 11 ] 22 [ 21 ] [ 36 ] 23 [ 39 ] 24 [ 21 ] [ 36 ] 25 [ 28 ] 26 [ 25 ] 27 [ 9 ] [ 14 ] 28 [ 17 ] [ 27 ] [ 29 ] [ 30 ] [ 32 ] [ 33 ] [ 35 ] 29 [ 37 ] 30 [ 16 ] [ 26 ] 31 [ 27 ] [ 29 ] [ 30 ] [ 33 ] 32 [ 20 ] [ 24 ] [ 34 ] [ 38 ] 33 [ 12 ] 34 [ 19 ] 35 [ 21 ] [ 36 ]  ©2005 Association for Computing Machinery |
