Table of Contents

• On Effective Conceptual Indexing and Similarity Search in Text DataCharu C. Aggarwal, Philip S. Yu. 3-10 [doi]
• Comparisons of Classification Methods for Screening Potential CompoundsAijun An, Yuanyuan Wang. 11-18 [doi]
• Knowledge Discovery from Diagrammatically Represented DataMichael Anderson. 19-26 [doi]
• Integrating E-Commerce and Data Mining: Architecture and ChallengesSuhail Ansari, Ron Kohavi, Llew Mason, Zijian Zheng. 27-34 [doi]
• Classification with Degree of Membership: A Fuzzy ApproachWai-Ho Au, Keith C. C. Chan. 35-42 [doi]
• Provably Fast Training Algorithms for Support Vector MachinesJosé L. Balcázar, Yang Dai, Osamu Watanabe. 43-50 [doi]
• Who Links to Whom: Mining Linkage between Web SitesKrishna Bharat, Bay-Wei Chang, Monika Rauch Henzinger, Matthias Ruhl. 51-58 [doi]
• Better Rules, Few Features: A Semantic Approach to Selecting Features from TextCatherine Blake, Wanda Pratt. 59-66 [doi]
• Significance Tests for Patterns in Continuous DataRichard J. Bolton, David J. Hand. 67-74 [doi]
• Distributed Web Mining Using Bayesian Networks from Multiple Data StreamsR. Chen, Krishnamoorthy Sivakumar, Hillol Kargupta. 75-82 [doi]
• A Hypergraph Based Clustering Algorithm for Spatial Data SetsJong-Sheng Cherng, Mei-Jung Lo. 83-90 [doi]
• Efficient Determination of Dynamic Split Points in a Decision TreeDavid Maxwell Chickering, Christopher Meek, Robert Rounthwaite. 91-98 [doi]
• Efficient Yet Accurate ClusteringManoranjan Dash, Kian-Lee Tan, Huan Liu. 99-106 [doi]
• A Min-max Cut Algorithm for Graph Partitioning and Data ClusteringChris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Ming Gu, Horst D. Simon. 107-114 [doi]
• Preprocessing Opportunities in Optimal Numerical Range PartitioningTapio Elomaa, Juho Rousu. 115-122 [doi]
• Using Artificial Anomalies to Detect Unknown and Known Network IntrusionsWei Fan, Matthew Miller, Salvatore J. Stolfo, Wenke Lee, Philip K. Chan. 123-130 [doi]
• Using Rule Sets to Maximize ROC PerformanceTom Fawcett. 131-138 [doi]
• A Synchronization Based Algorithm for Discovering Ellipsoidal Clusters in Large DatasetsHichem Frigui, Mohamed Ben Hadj Rhouma. 139-146 [doi]
• Functional Trees for ClassificationJoao Gama. 147-154 [doi]
• A Tight Upper Bound on the Number of Candidate PatternsFloris Geerts, Bart Goethals, Jan Van den Bussche. 155-162 [doi]
• Efficiently Mining Maximal Frequent ItemsetsKaram Gouda, Mohammed Javeed Zaki. 163-170 [doi]
• The DIAsDEM Framework for Converting Domain-Specific Texts into XML Documents with Data Mining TechniquesHenner Graubitz, Myra Spiliopoulou, Karsten Winkler. 171-178 [doi]
• A Scalable Algorithm for Clustering Sequential DataValerie Guralnik, George Karypis. 179-186 [doi]
• Clustering Validity Assessment: Finding the Optimal Partitioning of a Data SetMaria Halkidi, Michalis Vazirgiannis. 187-194 [doi]
• Automatic Topic Identification Using Webpage ClusteringXiaofeng He, Chris H. Q. Ding, Hongyuan Zha, Horst D. Simon. 195-202 [doi]
• Time Series Segmentation for Context Recognition in Mobile DevicesJohan Himberg, Kalle Korpiaho, Heikki Mannila, Johanna Tikanmäki, Hannu Toivonen. 203-210 [doi]
• Indiscernibility Degree of Objects for Evaluating Simplicity of Knowledge in the Clustering ProcedureShoji Hirano, Shusaku Tsumoto. 211-217 [doi]
• Mining Coverage-Based Fuzzy Rules by Evolutional ComputationTzung-Pei Hong, Yeong-Chyi Lee. 218-224 [doi]
• An Efficient Fuzzy C-Means Clustering AlgorithmMing-Chuan Hung, Don-Lin Yang. 225-232 [doi]
• Using Rough Sets Theory and Database Operations to Construct a Good Ensemble of Classifiers for Data Mining ApplicationsXiaohua Hu. 233-240 [doi]
• Fuzzy Data Mining: Effect of Fuzzy DiscretizationHisao Ishibuchi, Takashi Yamamoto, Tomoharu Nakashima. 241-248 [doi]
• The Computational Complexity of High-Dimensional Correlation SearchChris Jermaine. 249-256 [doi]
• Evaluating Boosting Algorithms to Classify Rare Classes: Comparison and ImprovementsMahesh V. Joshi, Vipin Kumar, Ramesh C. Agarwal. 257-264 [doi]
• An Agglomerative Hierarchical Clustering Using Partial Maximum Array and Incremental Similarity Computation MethodSung Young Jung, Taek-Soo Kim. 265-272 [doi]
• Distance Measures for Effective Clustering of ARIMA Time-SeriesKonstantinos Kalpakis, Dhiral Gada, Vasundhara Puttagunta. 273-280 [doi]
• Mining Decision Trees from Data Streams in a Mobile EnvironmentHillol Kargupta, Byung-Hoon Park. 281-288 [doi]
• An Online Algorithm for Segmenting Time SeriesEamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani. 289-296 [doi]
• AINE: An Immunological Approach to Data MiningThomas Knight, Jon Timmis. 297-304 [doi]
• Concise Representation of Frequent Patterns Based on Disjunction-Free GeneratorsMarzena Kryszkiewicz. 305-312 [doi]
• Frequent Subgraph DiscoveryMichihiro Kuramochi, George Karypis. 313-320 [doi]
• Statistical Considerations in Learning from DataHenry E. Kyburg Jr.. 321-328 [doi]
• Subject Classification in the Oxford English DictionaryZarrin Langari, Frank Wm. Tompa. 329-336 [doi]
• On Mining General Temporal Association Rules in a Publication DatabaseChang-Hung Lee, Cheng-Ru Lin, Ming-Syan Chen. 337-344 [doi]
• Preparations for Semantics-Based XML MiningJung-Won Lee, Kiho Lee, Won Kim. 345-352 [doi]
• Mining Image Features for Efficient Query ProcessingBeitao Li, Wei-Cheng Lai, Edward Y. Chang, Kwang-Ting Cheng. 353-360 [doi]
• Mining the Smallest Association Rule Set for PredictionsJiuyong Li, Hong Shen, Rodney W. Topor. 361-368 [doi]
• CMAR: Accurate and Efficient Classification Based on Multiple Class-Association RulesWenmin Li, Jiawei Han, Jian Pei. 369-376 [doi]
• Analyzing the Interestingness of Association Rules from the Temporal DimensionBing Liu, Yiming Ma, Ronnie Lee. 377-384 [doi]
• Closing the Loop: An Agenda- and Justification-Based Framework for Selecting the Next Discovery Task to PerformGary Livingston, John M. Rosenberg, Bruce G. Buchanan. 385-392 [doi]
• Closing the Loop: Heuristics for Autonomous DiscoveryGary Livingston, John M. Rosenberg, Bruce G. Buchanan. 393-400 [doi]
• Anchor Text Mining for Translation of Web QueriesWen-Hsiang Lu, Lee-Feng Chien, Hsi-Jian Lee. 401-408 [doi]
• Mining Mutually Dependent PatternsSheng Ma, Joseph L. Hellerstein. 409-416 [doi]
• The EQ Framework for Learning Equivalence Classes of Bayesian NetworksPaul Munteanu, Mohamed Bendou. 417-424 [doi]
• Visualizing Association Mining Results through Hierarchical ClustersSteven Noel, Vijay V. Raghavan, Chee-Hung Henry Chu. 425-432 [doi]
• Mining Constrained Association Rules to Predict Heart DiseaseCarlos Ordonez, Edward Omiecinski, Levien de Braal, Cesar A. Santana, Norberto F. Ezquerra, José A. Taboada, C. David Cooke, Elizabeth Krawczynska, Ernest V. Garcia. 433-440 [doi]
• H-Mine: Hyper-Structure Mining of Frequent Patterns in Large DatabasesJian Pei, Jiawei Han, Hongjun Lu, Shojiro Nishio, Shiwei Tang, Dongqing Yang. 441-448 [doi]
• FARM: A Framework for Exploring Mining Spaces with Multiple AttributesChang-Shing Perng, Haixun Wang, Sheng Ma, Joseph L. Hellerstein. 449-456 [doi]
• Neural Analysis of Mobile Radio Access NetworkKimmo Raivio, Olli Simula, Jaana Laiho. 457-466 [doi]
• Discovery of Association Rules in Tabular DataGraeme Richards, Victor J. Rayward-Smith. 465-472 [doi]
• Theory and Applications of Attribute DecompositionLior Rokach, Oded Maimon. 473-480 [doi]
• FlExPat: Flexible Extraction of Sequential PatternsPierre-Yves Rolland. 481-488 [doi]
• Interestingness PreProcessingSigal Sahar. 489-496 [doi]
• Data Analysis and Mining in Ordered Information TablesYing Sai, Yiyu Yao, Ning Zhong. 497-504 [doi]
• LPMiner: An Algorithm for Finding Frequent Itemsets Using Length-Decreasing Support ConstraintMasakazu Seno, George Karypis. 505-512 [doi]
• Document Clustering and Cluster Topic Extraction in Multilingual CorporaJoaquim Ferreira da Silva, João Mexia, Carlos Agra Coelho, José Gabriel Pereira Lopes. 513-520 [doi]
• Hierarchical Text Classification and EvaluationAixin Sun, Ee-Peng Lim. 521-528 [doi]
• Web Cartography for Online State Promotion: An Algorithm for Clustering Web ResourcesFrançois Velin, Pascale Kuntz, Henri Briand. 529-535 [doi]
• Maintenance of Sequential Patterns for Record DeletionChing-Yao Wang, Tzung-Pei Hong, Shian-Shyong Tseng. 536-541 [doi]
• SSDT: A Scalable Subspace-Splitting Classifier for Biased DataHaixun Wang, Philip S. Yu. 542-549 [doi]
• Meta-patterns: Revealing Hidden Periodic PatternsWei Wang 0010, Jiong Yang, Philip S. Yu. 550-557 [doi]
• Using Boosting to Simplify Classification ModelsVirginia Wheway. 558-565 [doi]
• Interestingness, Peculiarity, and Multi-Database MiningNing Zhong, Y. Y. Yao, Muneaki Ohshima, Setsuo Ohsuga. 566-576 [doi]
• Discovering Similar Patterns for Characterising Time Series in a Medical DomainFernando Alonso, Juan Pedro Caraça-Valente, Loïc Martínez, César Montes. 577-579 [doi]
• Creating Ensembles of ClassifiersNitesh V. Chawla, Steven Eschrich, Lawrence O. Hall. 580-581 [doi]
• Association Rules Enhanced Classification of Underwater Acoustic SignalJie Chen, Haiying Li, Shiwei Tang. 582-583 [doi]
• Efficient Splitting Rules Based on the Probabilities of Pre-assigned IntervalsJune-Suh Cho, Nabil R. Adam. 584-585 [doi]
• Inexact Field Learning: An Approach to Induce High Quality Rules from Low Quality DataHonghua Dai, Xiaoshu Hang, Gang Li. 586-588 [doi]
• Incremental Support Vector Machine ConstructionCarlotta Domeniconi, Dimitrios Gunopulos. 589-592 [doi]
• Mining Generalized Association Rules for Sequential and Path DataWolfgang Gaul, Lars Schmidt-Thieme. 593-596 [doi]
• Combining Labeled and Unlabeled Data for Text Classification with a Large Number of CategoriesRayid Ghani. 597-598 [doi]
• Dependency Derivation in Industrial Process DataDaniel Gillblad, Anders Holst. 599-602 [doi]
• Discovering Representative Episodal Association Rules from Event Sequences Using Frequent Closed Episode Sets and Event ConstraintsSherri K. Harms, Jitender S. Deogun, Jamil Saquer, Tsegaye Tadesse. 603-606 [doi]
• Text Clustering Based on Good AggregationsAndreas Hotho, Alexander Maedche, Steffen Staab. 607-608 [doi]
• Ad Hoc Association Rule Mining as SQL3 QueriesHasan M. Jamil. 609-612 [doi]
• Heuristic Optimization for Decentralized Frequent Itemset CountingViviane Crestana-Jensen, Nandit Soparkar. 613-614 [doi]
• Evolutionary Structure Learning Algorithm for Bayesian Network and Penalized Mutual Information MetricGang Li, Fu Tong, Honghua Dai. 615-616 [doi]
• Applications of Data Mining in HydrologyXu Liang, Yao Liang. 617-620 [doi]
• RPCL-Based Local PCA AlgorithmZhiyong Liu, Lei Xu. 621-622 [doi]
• Learning Automatic Acquisition of Subcategorization Frames Using Bayesian Inference and Support Vector MachinesManolis Maragoudakis, Katia Kermanidis, Nikos Fakotakis, George K. Kokkinakis. 623-625 [doi]
• Bayesian Data Mining on the Web with B-CoursePetri Myllymäki, Tomi Silander, Henry Tirri, Pekka Uronen. 626-629 [doi]
• An Experimental Comparison of Supervised and Unsupervised Approaches to Text SummarizationTadashi Nomoto, Yuji Matsumoto. 630-632 [doi]
• A Fast Algorithm to Cluster High Dimensional Basket DataCarlos Ordonez, Edward Omiecinski, Norberto F. Ezquerra. 633-636 [doi]
• Metric Rule Generation with Septic Shock Patient DataJürgen Paetz. 637-638 [doi]
• The Representative Basis for Association RulesViet Phan Luong. 639-640 [doi]
• Incremental Learning with Support Vector MachinesStefan Rüping. 641-642 [doi]
• A Clustering Method for Very Large Mixed Data SetsGuillermo Sánchez-Díaz, José Ruiz-Shulcloper. 643-644 [doi]
• Mining the Web with Active Hidden Markov ModelsTobias Scheffer, Christian Decomain, Stefan Wrobel. 645-646 [doi]
• A Simple KNN Algorithm for Text CategorizationPascal Soucy, Guy W. Mineau. 647-648 [doi]
• Measuring Real-Time Predictive ModelsSam Steingold, Richard Wherry, Gregory Piatetsky-Shapiro. 649-650 [doi]
• Incremental Learning of Bayesian Networks with Hidden VariablesFengzhan Tian, Hongwei Zhang, Yuchang Lu, Chunyi Shi. 651-652 [doi]
• Mining Frequent Closed Itemsets with the Frequent Pattern ListFan-Chen Tseng, Ching-Chi Hsu, Henry Chen. 653-654 [doi]
• Classification through Maximizing DensityHui Wang, Ivo Düntsch, David A. Bell, Dayou Liu. 655-656 [doi]
• An Immune Neural Network Used for ClassificationLei Wang, Licheng Jiao. 657-658 [doi]
• alpha-Surface and Its Application to Mining Protein DataXiong Wang. 659-662
• An Efficient Data Mining Technique for Discovering Interesting Sequential PatternsShow-Jane Yen, Yue-Shi Lee. 663-664 [doi]
• Fast Parallel Association Rule Mining without Candidacy GenerationOsmar R. Zaïane, Mohammad El-Hajj, Paul Lu. 665-668 [doi]
• A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other MethodsBernard Zenko, Ljupco Todorovski, Saso Dzeroski. 669-670 [doi]
• Mining California Vital Statistics DataDu Zhang, Quoc Luan Ha, Meiliu Lu. 671-672 [doi]
• A Pattern Decomposition (PD) Algorithm for Finding All Frequent Patterns in Large DatasetsQinghua Zou, Wesley W. Chu, David B. Johnson, Henry Chiu. 673-674 [doi]