Journal: SIGKDD Explorations

Volume 5, Issue 2

1 -- 5Gregory Piatetsky-Shapiro, Pablo Tamayo. Microarray data mining: facing the challenges
6 -- 15Vinay Nadimpally, Mohammed Javeed Zaki. A novel approach to determine normal variation in gene expression data
16 -- 22S. N. Mukherjee, Stephen J. Roberts, Peter Sykacek, Sarah J. Gurr. Gene ranking using bootstrapped P-values
23 -- 30Blaise Hanczar, Mélanie Courtine, Arriel Benis, Corneliu Henegar, Karine Clément, Jean-Daniel Zucker. Improving classification of microarray data using prototype-based feature selection
31 -- 36Richard Simon. Supervised analysis when the number of candidate features (p) greatly exceeds the number of cases (n)
38 -- 47Michael O Connell. Differential expression, class discovery and class prediction using S-PLUS and S+ArrayAnalyzer
48 -- 55Eric Bair, Robert Tibshirani. Machine learning methods applied to DNA microarray data can improve the diagnosis of cancer
56 -- 68Sandrine Dudoit, Mark J. van der Laan, Sündüz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng. Loss-based estimation with cross-validation: applications to microarray data analysis
69 -- 78Benny Y. M. Fung, Vincent T. Y. Ng. Classification of heterogeneous gene expression data
79 -- 90Daxin Jiang, Jian Pei, Aidong Zhang. Towards interactive exploration of gene expression patterns
91 -- 100Xintao Wu, Yong Ye, Liying Zhang. Graphical modeling based gene interaction analysis for microarray data
101 -- 112Patrick Glenisson, Janick Mathys, Bart De Moor. Meta-clustering of gene expression data and literature-based information
113 -- 121Hiroshi Mamitsuka, Yasushi Okuno, Atsuko Yamaguchi. Mining biologically active patterns in metabolic pathways using microarray expression profiles
122 -- 129Marla D. Curran, Hong Liu, Fan Long, Nanxiang Ge. Statistical methods for joint data mining of gene expression and DNA sequence database
130 -- 139Benjamin I. P. Rubinstein, Jon D. McAuliffe, Simon Cawley, Marimuthu Palaniswami, Kotagiri Ramamohanarao, Terence P. Speed. Machine learning in low-level microarray analysis
140 -- 148Tom Fawcett. In vivo spam filtering: a challenge problem for KDD
149 -- 151Johannes Gehrke, Paul Ginsparg, Jon M. Kleinberg. Overview of the 2003 KDD Cup
152 -- 153J. N. Manjunatha, K. R. Sivaramakrishnan, Raghavendra Kumar Pandey, M. Narasimha Murty. Citation prediction using time series approach KDD Cup 2003 (task 1)
154 -- 155Claudia Perlich, Foster J. Provost, Sofus A. Macskassy. Predicting citation rates for physics papers: constructing features for an ordered probit model
156 -- 157Sunita Sarawagi, V. G. Vinod Vydiswaran, Sumana Srinivasan, Kapil Bhudhia. Resolving citations in a paper repository
158 -- 159Martine Cadot, Joseph di Martino. A data cleaning solution by Perl scripts for the KDD Cup 2003 task 2
160 -- 162Janez Brank, Jure Leskovec. The Download Estimation task on KDD Cup 2003
163 -- 164Joel Carleton, Daragh Hartnett, Joseph Milana, Michinari Momma, Joseph Sirosh, Gabriela Surpi. Model Builder for Predictive Analytics & Fair Isaac s approach to KDD Cup 2003
165 -- 172Amy McGovern, Lisa Friedland, Michael Hay, Brian Gallagher, Andrew Fast, Jennifer Neville, David Jensen. Exploiting relational structure to understand publication patterns in high-energy physics
173 -- 178Shou-de Lin, Hans Chalupsky. Using unsupervised link discovery methods to find interesting facts and connections in a bibliography dataset
179 -- 184Shawndra Hill, Foster J. Provost. The myth of the double-blind review?: author identification using only citations
191 -- 196Usama M. Fayyad, Gregory Piatetsky-Shapiro, Ramasamy Uthurusamy. Summary from the KDD-03 panel: data mining: the next 10 years
197 -- 0Robert L. Grossman. KDD-2003 workshop on data mining standards, services and platforms (DM-SSP 03)
198 -- 199Mohammed Javeed Zaki, Jason Tsong-Li Wang, Hannu Toivonen. Data mining in bioinformatics: report on BIOKDD 03
200 -- 202Saso Dzeroski, Luc De Raedt, Stefan Wrobel. Multirelational data mining 2003: workshop report

Volume 5, Issue 1

1 -- 16Saso Dzeroski. Multi-relational data mining: an introduction
17 -- 30Hendrik Blockeel, Michèle Sebag. Scalability and efficiency in multi-relational data mining
31 -- 48Luc De Raedt, Kristian Kersting. Probabilistic logic learning
49 -- 58Thomas Gärtner. A survey of kernels for structured data
59 -- 68Takashi Washio, Hiroshi Motoda. State of the art of graph-based data mining
69 -- 79David Page, Mark Craven. Biological applications of multi-relational data mining
80 -- 83Pedro Domingos. Prospects and challenges for multi-relational data mining
84 -- 89Lise Getoor. Link mining: a new data mining challenge
90 -- 93Lawrence B. Holder, Diane J. Cook. Graph-based relational learning: current and future directions
94 -- 99John F. Roddick, Peter Fule, Warwick J. Graco. Exploratory medical knowledge discovery: experiences and issues
100 -- 101Saso Dzeroski, Luc De Raedt. Multi-relational data mining: the current frontiers