WhatToSee
- A Bayesian Framework for Regularized SVM Parameter Estimation Jens Gregor and Zhenqiu Liu University of Tennessee Department of Computer Science Knoxville, TN 37996-3450, USA jgregor@cs.utk.edu, zliu@utk.edu
- A Comparative Study of Linear and Nonlinear Feature Extraction Methods
- A Greedy Algorithm for Selecting Models in Ensembles
- A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks Pierre Geurts, Ibtissam El Khayat, Guy Leduc University of Liege,` Sart Tilman, B28 Liege` 4000 - Belgium {geurts, elkhayat, leduc}@montefiore.ulg.ac.be
- A Polygonal Line Algorithm based Nonlinear Feature Extraction Method Feng Zhang Texas A&M University College Station, Texas 77843 zhangfeng@neo.tamu.edu
- A Probabilistic Approach for Adapting Information Extraction Wrappers and Discovering New Attributes Tak-Lam Wong and Wai Lam Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin Hong Kong fg@se.cuhk.edu.hk wongtl,wlam
- A Transaction-based Neighbourhood-driven Approach to Quantifying Interestingness of Association Rules
- A biobjective model to select features with good classification quality and low cost Emilio Carrizosa Facultad de Matematicas. Universidad de Sevilla (Spain) ´ ecarrizosa@us.es Belen Martin-Barragan Facultad de Matematicas. Universidad de Sevilla (Spain) ´ belmart@us.es Dolores Romero Morales Said Business School. University of Oxford (United Kingdom) ¨ dolores.romero-morales@sbs.ox.ac.uk
- AGILE: A General Approach to Detect Transitions in Evolving Data Streams
- AVT-NBL: An Algorithm for Learning Compact and Accurate Naive Bayes ¨ Classifiers from Attribute Value Taxonomies and Data Jun Zhang and Vasant Honavar Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University Ames, Iowa 50011-1040, USA {jzhang, honavar}@cs.iastate.edu
- Active Feature-Value Acquisition for Classifier Induction
- Aligning Boundary in Kernel Space for Learning Imbalanced Dataset Gang Wu & Edward Y. Chang Department of Electrical & Computer Engineering University of California, Santa Barbara 93106 {gwu@engineering, echang@ece}.ucsb.edu
- Alpha Galois Lattices V´eronique Ventos L.R.I., UMR-CNRS 8623, Universite´ Paris-Sud, 91405 Orsay, France Henry Soldano L.I.P.N, UMR-CNRS 7030, Universite´ Paris-Nord, 93430 Villetaneuse, France Thibaut Lamadon L.R.I., UMR-CNRS 8623, Universite´ Paris-Sud, 91405 Orsay, France
- An Adaptive Density-Based Clustering Algorithm for Spatial Database with Noise Daoying Ma and Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: {daoma,azhang}@cse.buffalo.edu
- An Adaptive Learning Approach for Noisy Data Streams Fang Chu Yizhou Wang Carlo Zaniolo
- An Evaluation of Approaches to Classification Rule Selection Frans Coenen and Paul Leng Department of Computer Science, The University of Liverpool, Liverpool, L69 3BX frans,phl @csc.liv.ac.uk
- Analysis of Consensus Partition in Cluster Ensemble
- Attribute Measurement Policies for Time and Cost Sensitive Classification Andrew Arnt and Shlomo Zilberstein Department of Computer Science University of Massachusetts at Amherst Amherst, MA 01003 [arnt,shlomo]@cs.umass.edu
- Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
- Classifying Biomedical Citations without Labeled Training Examples Xiaoli Li*, Rohit Joshi, Sreeram Ramachandaran, Tze-Yun Leong* School of Computing, National University of Singapore Computer Science Program, Singapore MIT Alliance*
- Cluster Cores-based Clustering for High Dimensional Data
- Clustering on Demand for Multiple Data Streams Bi-Ru Dai, Jen-Wei Huang, Mi-Yen Yeh, and Ming-Syan Chen Department of Electrical Engineering National Taiwan University Taipei, Taiwan, ROC E-mail:mschen@cc.ee.ntu.edu.tw, {brdai, jwhuang, miyen}@arbor.ee.ntu.edu.tw
- Communication Efficient Construction of Decision Trees Over Heterogeneously Distributed Data
- Correlation Preserving Discretization
- Cost-Guided Class Noise Handling for Effective Cost-Sensitive Learning Xingquan Zhu and Xindong Wu Department of Computer Science, University of Vermont, Burlington VT 05405, USA {xqzhu, xwu}@cs.uvm.edu
- DRYADE: a new approach for discovering closed frequent trees in heterogeneous tree databases Alexandre Termier,£ Marie-Christine Rousset & Michele` Sebag CNRS & Universite´ Paris-Sud (LRI) - INRIA (Futurs) Building 490, Universite´ Paris-Sud, 91405 Orsay Cedex, France. termier, mcr, sebag @lri.fr
- Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams Wei Fan1 Yi-an Huang2 Philip S. Yu1
- Density Connected Clustering with Local Subspace Preferences Christian B¨ohm, Karin Kailing, Hans-Peter Kriegel, Peer Kr¨oger Institute for Computer Science, University of Munich, Germany {boehm,kailing,kriegel,kroegerp}@dbs.ifi.lmu.de
- Dependencies between transcription factor binding sites: comparison between ICA, NMF, PLSA and frequent sets Heli Hiisila¨ and Ella Bingham Neural Networks Research Centre, Laboratory of Computer and Information Science Helsinki University of Technology, P.O. Box 5400, FIN-02015 HUT, Finland heli.hiisila@hut.fi, ella@iki.fi
- Dependency Networks for Relational Data Jennifer Neville, David Jensen Computer Science Department University of Massachusetts Amherst Amherst, MA 01003 {jneville|jensen}@cs.umass.edu
- Detecting Patterns of Appliances from Total Load Data Using a Dynamic Programming Approach
- Detection of Significant Sets of Episodes in Event Sequences
- Discovery of Functional Relationships in Multi-relational Data using Inductive Logic Programming Alexessander Alves, Rui Camacho and Eugenio Oliveira LIACC, Rua do Campo Alegre, 823, 4150 Porto, Portugal FEUP, Rua Dr Roberto Frias, 4200-465 Porto, Portugal alves@ieee.org {rcamacho,eco}@fe.up.pt tel: +351 22 508 1849 fax: +351 22 508 1443
- Divide and Prosper: Comparing Models of Customer Behavior From Populations to Individuals
- Dynamic Classifier Selection for Effective Mining from Noisy Data Streams Xingquan Zhu, Xindong Wu, and Ying Yang Department of Computer Science, University of Vermont, Burlington VT 05405, USA {xqzhu, xwu, yyang}@cs.uvm.edu
- Dynamic Daily-living Patterns and Association Analyses in Tele-care Systems B.-S. Lee1, T. P. Martin1, N. P. Clarke1, B. Majeed2, and D. Nauck2
- Efficient Density-Based Clustering of Complex Objects Stefan Brecheisen, Hans-Peter Kriegel, Martin Pfeifle Institute for Computer Science, University of Munich, Germany {brecheisen, kriegel, pfeifle}@dbs.informatik.uni-muenchen.de
- Efficient Relationship Pattern Mining using Multi-relational Iceberg-cubes Dawit Yimam Seid, Sharad Mehrotra Department of Computer Science University of California, Irvine dseid,sharad @ics.uci.edu
- Estimation of False Negatives in Classification
- Evaluating Attraction in Spatial Point Patterns with an Application in the Field of Cultural History Marko Salmenkivi Helsinki Institute for Information Technology, Basic Research Unit P.O.Box 68, FINUniversity of Helsinki, Finland marko.salmenkivi@cs.helsinki.fi
- Evolutionary Algorithms for Clustering Gene-Expression Data* Eduardo R. Hruschka, Leandro N. de Castro, Ricardo J. G. B. Campello Universidade Católica de Santos (UniSantos) {erh,lnunes,campello}@unisantos.br
- Extensible Markov Model1 Margaret H. Dunham and Yu Meng
- Fast and Exact Out-of-Core K-Means Clustering Anjan Goswami Ruoming Jin
- Feature Selection via Supervised Model Construction Y Huang, PJ. McCullagh, ND. Black School of Computing and Mathematics, Faculty of Engineering, University of Ulster, Jordanstown, BT37 0QB, Northern Ireland, UK E-mail: yhuang@infj.ulst.ac.uk, {pj.mccullagh, nd.black}@ulst.ac.uk
- Filling-in Missing Objects in Orders Toshihiro Kamishima Shotaro Akaho National Institute of Advanced Industrial Science and Technology (AIST) AIST Tsukuba Central 2, Umezono 1-1-1, Tsukuba, Ibaraki, 305-8568 Japan mail@kamishima.net (http://www.kamishima.net/) s.akaho@aist.go.jp
- Finding Constrained Frequent Episodes Using Minimal Occurrences Xi MA1 HweeHwa PANG2 Kian-Lee TAN1
- GREW--A Scalable Frequent Subgraph Discovery Algorithm Michihiro Kuramochi and George Karypis Department of Computer Science and Engineering University of Minnesota {kuram, karypis}@cs.umn.edu
- Generation of Attribute Value Taxonomies from Data for Data-Driven Construction of Accurate and Compact Classifiers Dae-Ki Kang, Adrian Silvescu, Jun Zhang, and Vasant Honavar Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University, Ames, IA 50011 USA {dkkang, silvescu, junzhang, honavar}@iastate.edu
- Hybrid pre-query term expansion using Latent Semantic Analysis Laurence A. F. Park Kotagiri Ramamohanarao ARC Centre for Perceptive and Intelligent Machines Department of Computer Science The University of Melbourne {lapark,rao}@cs.mu.oz.au
- IRC: An Iterative Reinforcement Categorization Algorithm for Interrelated Web Objects
- Improving Text Classification using Local Latent Semantic Indexing Tao Liu Zheng Chen* Benyu Zhang* Wei-ying Ma* Gongyi Wu Nankai University, China * Microsoft Research Asia Nankai University, China liut@office.nankai.edu.cn *{zhengc,byzhang,wyma}@microsoft.com wgy@nankai.edu.cn
- Improving the Reliability of Decision Tree and Naive Bayes Learners
- Incremental Mining of Frequent XML Query Patterns
- Integrating Multi-Objective Genetic Algorithms into Clustering for Fuzzy Association Rules Mining
- LOADED: Link-based Outlier and Anomaly Detection in Evolving Data Sets
- Learning Conditional Independence Tree for Ranking Jiang Su and Harry Zhang Faculty of Computer Science, University of New Brunswick P.O. Box 4400, Fredericton, NB, Canada E3B 5A3 hzhang@unb.ca
- Learning Rules from Highly Unbalanced Data Sets
- Learning Weighted Naive Bayes with Accurate Ranking
- MMAC: A New Multi-class, Multi-label Associative Classification Approach
- MMSS: Multi-modal Story-oriented Video Summarization£ Jia-Yu Pan, Hyungjeong Yang, Christos Faloutsos Computer Science Department Carnegie Mellon University jypan, hjyang, christos @cs.cmu.edu
- Mass Spectrum Labeling: Theory and Practice Z. Huang, L. Chen, J-Y. Cai, D. Gross*, D. Musicant*, R. Ramakrishnan, J. Schauer*, S.J. Wright
- Matching in Frequent Tree Discovery Bjorn Bringmann ¨ Machine Learning Lab, University of Freiburg Georges-Kohler-Alle, Geb. 079, 79098 Freiburg, Germany ¨ bbringma@informatik.uni-freiburg.de
- Metric Incremental Clustering of Nominal Data
- Mining Associations by Linear Inequalities Tsay Young ('T. Y.') Lin Department of Computer Science San Jose State University San Jose, CA 95192, USA tylin@cs.sjsu.edu
- Mining Frequent Closed Patterns in Microarray Data Gao Cong, Kian-Lee Tan, Anthony K.H. Tung, Feng Pan School of Computing National University of Singapore 3 Science Drive 2, Singapore {conggao, atung, tankl, panfeng}@comp.nus.edu.sg
- Mining Frequent Itemsets from Secondary Memory Gosta Grahne and Jianfei Zhu ¨ Concordia University, Montreal, Canada {grahne, j zhu}@cs.concordia.ca
- Mining Generalized Substructures from a Set of Labeled Graphs Akihiro Inokuchi Tokyo Research Laboratory, IBM Japan 1623-14, Shimo-tsuruma, Yamato, Kanagawa, 242-8502, Japan inokuchi@jp.ibm.com
- Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization Chenyong Hu1,Benyu Zhang2,Shuicheng Yan3,Qiang Yang4,Jun Yan3,Zheng Chen2,Wei-Ying Ma2
- Mining web data to create online navigation recommendations Juan D. Velasquez1 , Alejandro Bassi2 , Hiroshi Yasuda1 and Terumasa Aoki1 University of Tokyo, {jvelasqu,yasuda,aoki}@mpeg.rcast.u-tokyo.ac.jp
- Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
- Non-Redundant Data Clustering David Gondek Thomas Hofmann Department of Computer Science, Brown University Providence, RI 02912 USA {dcg,th}@cs.brown.edu
- On Closed Constrained Frequent Pattern Mining
- On Ranking Refinements in the Step-by-step Searching through a Product Catalogue Nenad Stojanovic Institute AIFB, University of Karlsruhe, Germany nst@aifb.uni-karlsruhe.de
- Orthogonal Decision Trees Hillol Kargupta£and Haimonti Dutta Department of Computer Science and Electrical Engineering University of Maryland Baltimore County, 1000 Hilltop Circle Baltimore, MD 21250 hillol, hdutta1 @cs.umbc.edu
- Pei Sun University of Sydney School of Information Technologies Sydney, NSW, Australia psun2712@it.usyd.edu.au
- Predicting Density-Based Spatial Clusters Over Time Chih Lai Nga T. Nguyen Graduate Programs in Software Engineering University of St. Thomas St. Paul, MN 55125 clai@stthomas.edu ntnguyen1@stthomas.edu
- Privacy-Preserving Outlier Detection
- Privacy-Sensitive Bayesian Network Parameter Learning
- Probabilistic Principal Surfaces for Yeast Gene Microarray Data Mining Antonino Staiano, Lara De Vinco, Angelo Ciaramella, Giancarlo Raiconi, Roberto Tagliaferri Dipartimento di Matematica ed Informatica Universita` di Salerno Via Ponte don Melillo, 84084 Fisciano (Sa), Italy {astaiano, ciaram, gianni, robtag}@unisa.it Roberto Amato, Giuseppe Longo, Ciro Donalek, Gennaro Miele Dipartimento di Scienze Fisiche Universita` Federico II di Napoli and INFN Napoli Unit Polo delle Scienze e della Tecnologia via Cintia 6, 80136 Napoli, Italy {longo, donalek, miele}@na.infn.it Diego Di Bernardo Telethon Institute for Genetics and Medicine Via Pietro Castellino 111 I-80131 Napoli, Italy dibernard@tigem.it
- Quantitative Association Rules Based on Half-Spaces: An Optimization Approach Ulrich Ruckert, Lothar Richter, and Stefan Kramer ¨ Technische Universitat Munchen ¨ ¨ Institut fur Informatik/I12 ¨ Boltzmannstr. 3, D-85748 Garching b. Munchen, Germany ¨ {rueckert, richter, kramer}@in.tum.de
- Query-Driven Support Pattern Discovery for Classification Learning Yiqiu Han and Wai Lam Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin, Hong Kong
- Relational Peculiarity Oriented Data Mining Ning Zhong½¾¿, Muneaki Ohshima½¾¾
- Revealing True Subspace Clusters in High Dimensions Jinze Liu, Karl Strohmaier, and Wei Wang Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 {liuj, strohma, weiwang}@cs.unc.edu
- SCHISM: A New Approach for Interesting Subspace Mining Karlton Sequeira and Mohammed Zaki
- SUMMARY: Efficiently Summarizing Transactions for Clustering Jianyong Wang and George Karypis
- SVD based Term Suggestion and Ranking System
- SVM and Graphical Algorithms: a Cooperative Approach François Poulet ESIEA P ECD BP 0339 53003 Laval Cedex - France poulet@esiea-ouest.fr
- Scalable Construction of Topic Directory with Nonparametric Closed Termset Mining
- Scalable Multi-Relational Association Mining
- Semi-Supervised Mixture-of-Experts Classification
- Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas
- Sparse Kernel Least Squares Classifier Ping Sun School of Computer Science The University of Birmingham Birmingham, B15 2TT, U.K. P.Sun@cs.bham.ac.uk
- Subspace Selection for Clustering High-Dimensional Data Christian Baumgartner, Claudia Plant University for Health Sciences, Medical Informatics and Technology, Innsbruck, Austria {christian.baumgartner,claudia.plant}@umit.at Karin Kailing, Hans-Peter Kriegel, Peer Kr¨oger Institute for Computer Science, University of Munich, Germany {kailing,kriegel,kroegerp}@dbs.ifi.lmu.de
- Supervised Latent Semantic Indexing for Document Categorization Jian-Tao Sun1, Zheng Chen2, Hua-Jun Zeng2, Yu-Chang Lu1, Chun-Yi Shi1, Wei-Ying Ma2
- Test-Cost Sensitive Naive Bayes Classification
- Text Classification by Boosting Weak Learners based on Terms and Concepts
- The Anatomy of a Hierarchical Clustering Engine
- Transduction and typicalness for quality assessment of individual classifications in machine learning and data mining Matjaz Kukar University of Ljubljana, Faculty of Computer and Information Science, Trza ska 25, SI-1001 Ljubljana, Slovenia matjaz.kukar@fri.uni-lj.si
- Unimodal Segmentation of Sequences Niina Haiminen and Aristides Gionis Helsinki Institute for Information Technology, BRU Department of Computer Science University of Helsinki, Finland first.lastname@cs.helsinki.fi
- Using Emerging Patterns and Decision Trees in Rare-class Classification Hamad Alhammady and Kotagiri Ramamohanarao Department of Computer Science and Software Engineering The University of Melbourne, Australia Email: {hhammady, rao}@cs.mu.oz.au
- Using Representative-Based Clustering for Nearest Neighbor Dataset Editing