WhatToSee

Index of ICDM 2005

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• A Bernoulli Relational Model for Nonlinear Embedding Gang Wang1, Hui Zhang2, Zhihua Zhang1, Frederick H. Lochovsky1 Hong Kong University of Science and Technology, Hong Kong
• A Border-Based Approach for Hiding Sensitive Frequent Itemsets
• A Framework for Semi-Supervised Learning based on Subjective and Objective Clustering Criteria
• A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data Hans-Peter Kriegel, Peer Kr¨oger, Matthias Renz, Sebastian Wurst Institute for Computer Science, University of Munich {kriegel,kroegerp,renz,wurst}@dbs.ifi.lmu.de
• A Heterogeneous Field Matching Method for Record Linkage Steven N. Minton and Claude Nanjo Fetch Technologies 2041 Rosecrans Ave., Suite 245 El Segundo, CA 90245 {sminton, cnanjo}@fetch.com Craig A. Knoblock, Martin Michalowski, and Matthew Michelson University of Southern California Information Sciences Institute, 4676 Admiralty Way Marina del Rey, CA 90292 USA {knoblock, martinm, michelso}@isi.edu
• A Join-less Approach for Co-location Pattern Mining: A Summary of Results Jin Soung Yoo , Shashi Shekhar, Mete Celik Computer Science Department, University of Minnesota, Minneapolis, MN, USA jyoo,shekhar,mcelik@cs.umn.edu
• A Levelwise Search Algorithm for Interesting Subspace Clusters
• A Preference Model for Structured Supervised Learning Tasks Fabio Aiolli Dip. di Matematica Pura e Applicata, Universita` di Padova Via G. Belzoni 7, 35131 Padova aiolli@math.unipd.it
• A Random Walk through Human Associations Raz Tamir The Hebrew University of Jerusalem raz.tamir@gmail.com
• A Scalable Collaborative Filtering Framework based on Co-clustering
• A Visual Data Mining Framework for Convenient Identification
• A new algorithm for finding minimal sample uniques for use in statistical disclosure assessment
• AMIOT: Induced Ordered Tree Mining in Tree-structured Databases
• Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience Abraham Bagherjeiran, Christoph F. Eick, Chun-Sheng Chen, Ricardo Vilalta
• Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping
• Alternate Representation of Distance Matrices for Characterization of Protein Structure Keith Marsolo and Srinivasan Parthasarathy The Ohio State University Department of Computer Science and Engineering Contact: srini@cse.ohio-state.edu
• An Algorithm for In-Core Frequent ltemset Mining on Streaming Data
• An Expected Utility Approach to Active Feature-value Acquisition
• An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias Wei Fan1 Ian Davidson2 Bianca Zadrozny1 Philip S. Yu1
• An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation
• Anomaly Intrusion Detection using Multi-Objective Genetic Fuzzy System and Agent-based Evolutionary Computation Framework Chi-Ho Tsang1 Sam Kwong2 Hanli Wang1 Department of Computer Science, City University of Hong Kong 83 Tat Chee Avenue, Kowloon, Hong Kong
• Approximate Inverse Frequent Itemset Mining: Privacy, Complexity, and Approximation Yongge Wang Xintao Wu UNC Charlotte, {yonwang, xwu}@uncc.edu
• Atomic Wedgie: Efficient Query Filtering for Streaming Time Series
• Automatically Mining Result Records from Search Engine Response Pages
• Average Number of Frequent (Closed) Patterns in Bernouilli and Markovian Databases Lo¨ick LHOTE, Franc¸ois RIOULT, Arnaud SOULET GREYC, CNRS - UMR 6072, Universite´ de Caen Basse-Normandie F-14032 Caen cedex, France {forename.surname}@info.unicaen.fr
• Bagging with Adaptive Costs
• Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning Thomas Osugi, Deng Kun, and Stephen Scott Dept. of Computer Science 256 Avery Hall University of Nebraska Lincoln, NE 68588-0115 {tosugi,kdeng,sscott}@cse.unl.edu
• Bias Analysis in Text Classification for Highly Skewed Data Lei Tang and Huan Liu Department of Computer Science & Engineering Arizona State University Tempe, AZ 85287-8809, USA {l.tang, hliu}@asu.edu
• Bifold Constraint-Based Mining by Simultaneous Monotone and Anti-Monotone Checking Mohammad El-Hajj, Osmar R. Za¨iane, Paul Nalos Department of Computing Science, University of Alberta Edmonton, AB, Canada mohammad, zaiane, nalos @cs.ualberta.ca
• Bit Reduction Support Vector Machine
• Blocking Anonymity Threats Raised by Frequent Itemset Mining Maurizio Atzori Francesco Bonchi Fosca Giannotti Dino Pedreschi
• CLUGO: A Clustering Algorithm for Automated Functional Annotations Based on Gene Ontology In-Yee Lee1,2, Jan-Ming Ho2, Ming-Syan Chen1
• CLUMP: A Scalable and Robust Framework for Structure Discovery
• CTC - Correlating Tree Patterns for Classification Albrecht Zimmermann Bjorn¨ Bringmann Machine Learning Lab, Albert-Ludwigs-University Freiburg, Georges-Kohler¨ -Allee 79, 79110 Freiburg, Germany E-mail: {azimmerm,bbringma}@informatik.uni-freiburg.de
• CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns Carson Kai-Sang Leung Quamrul I. Khan
• Categorization and Keyword Identification of Unlabeled Documents Ning Kang Carlotta Domeniconi Daniel Barbara´ ISE Department George Mason University
• Classifier Fusion Using Shared Sampling Distribution For Boosting Costin Barbu, Raja Iqbal and Jing Peng Department of Electrical Engineering and Computer Science Tulane University New Orleans LA-70118 {barbu,iqbal,jp}@eecs.tulane.edu
• CloseMiner: Discovering Frequent Closed Itemsets using Frequent Closed Tidsets
• CoLe: ACooperativeDataMiningApproach and Its Application to Early Diabetes Detection
• Combining Multiple Clusterings by Soft Correspondence
• Compound Classification Models for Recommender Systems Lars Schmidt-Thieme Institute of Computer Science, University of Freiburg, Germany lst@informatik.uni-freiburg.de
• Discovering Frequent Arrangements of Temporal Intervals
• Discriminant Analysis: A Unified Approach
• Discriminatively Trained Markov Model for Sequence Classification
• Effective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree Approaches Wei Fan1 Ed Greengrass2 Joe McCloskey2 Philip S. Yu1 Kevin Drummey2
• Effective and Efficient Distributed Model-based Clustering Hans-Peter Kriegel, Peer Kroger¨ , Alexey Pryakhin, Matthias Schubert Institute for Computer Science, University of Munich, Germany {kriegel,kroegerp,pryakhin,schubert}@dbs.ifi.lmu.de
• Efficient mining of high branching factor attribute trees Alexandre Termier1, Marie-Christine Rousset2, Michele Sebag2, ` Kouzou Ohara1, Takashi Washio1 & Hiroshi Motoda1
• Efficiently Mining Frequent Closed Partial Orders (Extended Abstract) Jian Pei1 Jian Liu2 Haixun Wang3 Ke Wang1 Philip S. Yu3 Jianyong Wang4
• Example-Based Robust Outlier Detection in High Dimensional Datasets
• Extracting Frequent Subsequences from a Single Long Data Sequence: A Novel Anti-Monotonic Measure and a Simple On-Line Algorithm Koji Iwanuma, Ryuichi Ishihara, Yo Takano, Hidetomo Nabeshima Department of Computer Science and Media Engineering University of Yamanashi 4-3-11 Takeda, Kofu-shi 400-8511, Japan {iwanuma,ishihara,takano,nabesima}@iw.media.yamanashi.ac.jp
• F S3: A Random Walk based Free-Form Spatial Scan Statistic for Anomalous Window Detection Vandana P. Janeja and Vijayalakshmi Atluri MSIS Department and CIMIC, Rutgers University {vandana,atluri}@cimic.rutgers.edu Abstract
• Face Recognition Using Landmark-based Bidimensional Regression
• Fast Frequent String Mining Using Suffix Arrays
• Feature Selection for Building Cost-Effective Data Stream Classifiers (Extended Abstract) Like Gao, X. Sean Wang Department of Computer Science, University of Vermont, Vermont, USA {lgao, xywang}@cs.uvm.edu
• Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
• Finding Representative Set from Massive Data
• Focused Community Discovery
• Generalizing the Notion of Confidence Michael Steinbach and Vipin Kumar Department of Computer Science and Engineering, University of Minnesota 4-192 EE/CSci Building, 200 Union Street SE Minneapolis, MN 55455 {steinbach, kumar}@cs.umn.edu
• Gradual Model Generator for Single-pass Clustering
• HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
• Handling Generalized Cost Functions in the Partitioning Optimization Problem Through Sequential Binary Programming
• Hierarchical Density-Based Clustering of Uncertain Data
• Hierarchy-Regularized Latent Semantic Indexing Yi Huang1, Kai Yu2, Matthias Schubert1, Shipeng Yu1, Volker Tresp2, Hans-Peter Kriegel1 huang@cip.ifi.lmu.de {kai.yu,volker.tresp}@siemens.com {schubert,spyu,kriegel}@dbs.ifi.lmu.de
• Higher-Order Web Link Analysis Using Multilinear Algebra Tamara G. Kolda, Brett W. Bader, and Joseph P. Kenny Sandia National Laboratories Livermore, CA and Albuquerque, NM {tgkolda,bwbader,jpkenny}@sandia.gov
• Hot Item Mining and Summarization from Multiple Auction Web Sites Tak-Lam Wong and Wai Lam Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin, Hong Kong wongtl,wlam @se.cuhk.edu.hk
• Improving Automatic Query Classification via Semi-supervised Learning
• Instability of Classifiers on Categorical Data
• Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering Jie Yin and Qiang Yang Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong, China {yinjie, qyang}@cs.ust.hk
• Kernel-Density-Based Clustering of Time Series Subsequences Using a Continuous Random-Walk Noise Model Anne Denton Department of Computer Science North Dakota State University Fargo, North Dakota 58105-5164, USA anne.denton@ndsu.edu
• Labeling Unclustered Categorical Data into Clusters Based on the Important Attribute Values Hung-Leng Chen, Kun-Ta Chuang and Ming-Syan Chen Department of Electrical Engineering National Taiwan University Taipei, Taiwan, ROC E-mail: {kidd,doug}@arbor.ee.ntu.edu.tw, mschen@cc.ee.ntu.edu.tw
• Learning Functional Dependency Networks based on Genetic Programming
• Learning Instance Greedily Cloning Naive Bayes for Ranking
• Leveraging Relational Autocorrelation with Latent Group Models Jennifer Neville, David Jensen Department of Computer Science, University of Massachusetts Amherst, MA 01003 {jneville|jensen}@cs.umass.edu
• Making Logistic Regression A Core Data Mining Tool With TR-IRLS
• Making Subsequence Time Series Clustering Meaningful Jason R. Chen Department of Information Engineering Research School of Information Science and Engineering The Australian National University Canberra, ACT, 0200, Australia Jason.Chen@anu.edu.au
• Merging Interface Schemas on the Deep Web via Clustering Aggregation
• Mining Approximate Frequent Itemsets from Noisy Data
• Mining Frequent Spatio-temporal Sequential Patterns Huiping Cao, Nikos Mamoulis, and David W. Cheung Department of Computer Science The University of Hong Kong Pokfulam Road, Hong Kong {hpcao, nikos, dcheung}@cs.hku.hk
• Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints Xiaonan Ji James Bailey Guozhu Dong
• Mining Ontological Knowledge from Domain-Specific Text Documents Xing Jiang and Ah-Hwee Tan School of Computer Engineering, Nanyang Technological University Nanyang Avenue, Singapore 639798 {jian0008,asahtan}@ntu.edu.sg
• Mining Patterns That Respond to Actions
• Mining Patterns of Change in Remote Sensing Image Databases Marcelino Pereira S. Silva1,2, Gilberto Câmara2, Ricardo Cartaxo M. Souza2, Dalton M. Valeriano2, Maria Isabel S. Escada2
• Mining Quantitative Frequent Itemsets Using Adaptive Density-based Subspace Clustering Takashi Washio, Yuki Mitsunaga and Hiroshi Motoda The Institute for Scientific and Industrial Research, Osaka University 8-1, Mihogaoka, Ibaraki City, Osaka, 567-0047, Japan washio@ar.sanken.osaka-u.ac.jp
• Mining chains of relations Foto Afrati
• Modeling Multiple Time Series for Anomaly Detection Philip K. Chan and Matthew V. Mahoney Department of Computer Sciences Florida Institute of Technology Melbourne, FL 32901 pkc, mmahoney@cs.fit.edu
• Multi-Stage Classification Ted E. Senator DARPA/IPTO* tsenator@darpa.mil
• Neighborhood Formation and Anomaly Detection in Bipartite Graphs
• OBTAINING BEST PARAMETER VALUES FOR ACCURATE CLASSIFICATION Frans Coenen and Paul Leng Department of Computer Science, The University of Liverpool, Liverpool, L69 3BX frans,phl @csc.liv.ac.uk
• On Feature Selection through Clustering
• On Learning Asymmetric Dissimilarity Measures
• On Reducing Classifier Granularity in Mining Concept-Drifting Data Streams
• On the Stationarity of Multivariate Time Series for Correlation-Based Data Analysis Kiyoung Yang and Cyrus Shahabi Computer Science Department University of Southern California Los Angeles, CA 90089-0781 [kiyoungy,shahabi]@usc.edu
• On the Tractability of Rule Discovery from Distributed Data Martin Scholz Artificial Intelligence Group Department of Computer Science University of Dortmund, Germany scholz@ls8.cs.uni-dortmund.de
• Online Hierarchical Clustering in a Data Warehouse Environment Elke Achtert, Christian Bo¨hm, Hans-Peter Kriegel, Peer Kr¨oger Institute for Computer Science, University of Munich {achtert,boehm,kriegel,kroegerp}@dbs.ifi.lmu.de
• Optimizing Constraint-Based Mining by Automatically Relaxing Constraints
• Orthogonal Neighborhood Preserving Projections E. Kokiopoulou and Y. Saad Computer Science and Engineering Department University of Minnesota Minneapolis, MN 55455, USA. {kokiopou, saad}@cs.umn.edu
• Pairwise Symmetry Decomposition Method for Generalized Covariance Analysis Tsuyoshi Ide´ IBM Research, Tokyo Research Laboratory 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan goodidea@jp.ibm.com
• Parallel algorithms for distance-based and density-based outliers
• Parameter-FreeSpatialDataMiningUsingMDL
• Partial Elastic Matching of Time Series Longin Jan Latecki Vasileios Megalooikonomou Qiang Wang Rolf Lakaemper Computer and Information Science Dept., Temple University Philadelphia, PA 19094, USA { latecki, vasilis, lakamper, qwang }@temple.edu C. A. Ratanamahatana E. Keogh Computer Science and Engineering Dept., University of California Riverside, CA 92521 { ratana, eamonn }@cs.ucr.edu
• Partial Ensemble Classifiers Selection for Better Ranking Jin Huang Charles X. Ling Department of Computer Science The University of Western Ontario London, Ontario, Canada N6A 5B7 jhuang33, cling @csd.uwo.ca
• Predicting Software Escalations with Maximum ROI Charles X. Ling1, Shengli Sheng1, Tilmann Bruckhaus2, Nazim H. Madhavji1
• Privacy Preserving Data Classification with Rotation Perturbation Keke Chen Ling Liu College of Computing, Georgia Institute of Technology {kekechen, lingliu}@cc.gatech.edu
• Privacy-Preserving Frequent Pattern Mining Across Private Databases
• Process Diagnosis via Electrical-Wafer-Sorting Maps Classification
• Pruning Social Networks Using Structural Properties and Descriptive Attributes
• Ranking-Based Evaluation of Regression Models Saharon Rosset, Claudia Perlich, Bianca Zadrozny IBM T. J. Watson Research Center P. O. Box 218 Yorktown Heights, NY 10598 {srosset, reisz, zadrozny}@us.ibm.com
• SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease using Spatial Information
• Segment-Based Injection Attacks against Collaborative Filtering Recommender Systems Robin Burke, Bamshad Mobasher, Runa Bhaumik, Chad Williams Center for Web Intelligence, DePaul University School of Computer Science, Telecommunication, and Information Systems Chicago, Illinois, USA {rburke, mobasher, rbhaumik, cwilli43}@cs.depaul.edu
• Semi-supervised Mixture of Kernels via LPBoost Methods Jinbo Bi Glenn Fung Murat Dundar Bharat Rao Computer Aided Diagnosis and Therapy Solutions Siemens Medical Solutions, Malvern, PA 19355 jinbo.bi, glenn.fung, murat.dundar, bharat.rao@siemens.com
• Semi­Supervised Clustering with Metric Learning using Relative Comparisons Nimit Kumar, Krishna Kummamuru, Deepa Paranjpe IBM India Research Lab, Block-1, Indian Institute of Technology, New Delhi-110016, INDIA. nimitk, kkummamu, dparanjp@in.ibm.com
• Sequential Pattern Mining in Multiple Streams Gong Chen Xindong Wu Xingquan Zhu
• Sharing Classifiers among Ensembles from Related Problem Domains
• Shortest-path kernels on graphs Karsten M. Borgwardt and Hans-Peter Kriegel Institute for Computer Science Ludwig-Maximilians-University Munich Oettingenstr. 67, 80538 Munich, Germany {kb|kriegel}@dbs.ifi.lmu.de
• Spatial Clustering Of Chimpanzee Locations For Neighborhood Identification Sandeep Mane , Carson Murray¶§, Shashi Shekhar, Jaideep Srivastava and Anne Pusey¶§
• Speculative Markov Blanket Discovery for Optimal Feature Selection
• Stability of Feature Selection Algorithms Alexandros Kalousis, Julien Prados, Melanie Hilario University of Geneva, Computer Science Department Rue General Dufour 24, 1211 Geneva 4, Switzerland {kalousis, prados, hilario}@cui.unige.ch
• Summarization - Compressing Data into an Informative Representation
• Supervised Ordering -- An Empirical Survey
• Supervised Tensor Learning Dacheng Tao1, Xuelong Li1, Weiming Hu2, Stephen Maybank1, and Xindong Wu3
• Suppressing Data Sets to Prevent Discovery of Association Rules
• Template-Based Privacy Preservation in Classification Problems
• Text Classification with Evolving Label-sets
• Text Representation: from Vector to Tensor* Ning Liu1, Benyu Zhang2, Jun Yan3, Zheng Chen2, Wenyin Liu4, Fengshan Bai1, Leefeng Chien5
• Training Support Vector Machines using Gilbert's Algorithm
• Triple Jump Acceleration for the EM Algorithm
• Usage-based PageRank for Web Personalization Magdalini Eirinaki, Michalis Vazirgiannis
• Using Information-theoretic Measures to Assess Association Rule Interestingness Julien Blanchard, Fabrice Guillet, Regis Gras, Henri Briand ´ LINA (FRE 2729 CNRS) ­ Polytechnic School of Nantes University La Chantrerie BP 50609 ­ 44306 Nantes cedex 3 ­ France {julien.blanchard,fabrice.guillet,henri.briand,regis.gras}@polytech.univ-nantes.fr
• ViVo: Visual Vocabulary Construction for Mining Biomedical Images
• Visualizing Global Manifold Based on Distributed Local Data Abstractions
• WARP: Time Warping for Periodicity Detection
• X-mHMM: An Efficient Algorithm for Training Mixtures of HMMs when the Number of Mixtures is Unknown
• eMailSift: Email Classification Based on Structure and Content Manu Aery and Sharma Chakravarthy IT Laboratory and CSE Department The University of Texas at Arlington {aery, sharma} @ cse.uta.edu