Data Mining: A Heuristic Approach
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[1] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[2] R. Prim. Shortest connection networks and some generalizations , 1957 .
[3] C. N. Liu,et al. Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.
[4] Srinivasan Parthasarathy,et al. Parallel Data Mining for Association Rules on Shared-Memory Multi-Processors , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.
[5] John Stewart,et al. Self and Nonself Revisited: Lessons from Modelling the Immune Network , 1995, ECAL.
[6] P. Matzinger,et al. Immunology. Memories are made of this? , 1994, Nature.
[7] Ulises Cortés,et al. A parallel algorithm for building possibilistic causal networks , 1998, Int. J. Approx. Reason..
[8] Pedro M. Domingos. Why Does Bagging Work? A Bayesian Account and its Implications , 1997, KDD.
[9] Shumeet Baluja,et al. A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .
[10] Alex Alves Freitas,et al. Discovering comprehensible classification rules by using Genetic Programming: a case study in a medical domain , 1999, GECCO.
[11] Dunja Mladenic,et al. Feature Subset Selection in Text-Learning , 1998, ECML.
[12] Luca Maria Gambardella,et al. Ant Algorithms for Discrete Optimization , 1999, Artificial Life.
[13] Jon Timmis,et al. aiVIS - Artificial Immune Network Visualisation , 2001 .
[14] Craig W. Reynolds. Evolution of obstacle avoidance behavior: using noise to promote robust solutions , 1994 .
[15] Rakesh Agrawal,et al. Parallel Mining of Association Rules , 1996, IEEE Trans. Knowl. Data Eng..
[16] Scott Robert Ladd,et al. Genetic algorithms in C , 1995 .
[17] P. Legendre,et al. The generation of random ultrametric matrices representing dendrograms , 1991 .
[18] David E. Goldberg,et al. The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..
[19] William B. Langdon,et al. Size fair and homologous tree genetic programming crossovers , 1999 .
[20] William B. Langdon,et al. Application of Genetic Programming to Induction of Linear Classification Trees , 2000, EuroGP.
[21] Peter Clark,et al. The CN2 induction algorithm , 2004, Machine Learning.
[22] Alan J. Miller. Subset Selection in Regression , 1992 .
[23] Alfred Ultsch,et al. Self Organizing Neural Networks perform different from statistical k-means clustering , 2003 .
[24] N. J. Radcliffe,et al. GA-MINER: Parallel Data Mining with Hierarchical Genetic Algorithms Final Report , 1995 .
[25] John E. Hunt,et al. Learning using an artificial immune system , 1996 .
[26] Srinivasan Parthasarathy,et al. Memory Placement Techniques for Parallel Association Mining , 1998, KDD.
[27] Leandro Nunes de Castro,et al. An Overview of Artificial Immune Systems , 2004 .
[28] Luisa Franconi,et al. Comparison of a genetic algorithm and simulated annealing in an application to statistical image reconstruction , 1997, Stat. Comput..
[29] F.J. Von Zuben,et al. An improving pruning technique with restart for the Kohonen self-organizing feature map , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[30] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[31] Pat Langley,et al. Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..
[32] Peter J. Bentley,et al. The Human Immune System and Network Intrusion Detection , 1999 .
[33] G. Bortolan,et al. The problem of linguistic approximation in clinical decision making , 1988, Int. J. Approx. Reason..
[34] David Wai-Lok Cheung,et al. Effect of Data Distribution in Parallel Mining of Associations , 1999, Data Mining and Knowledge Discovery.
[35] Alex Alves Freitas. Towards Large-Scale Knowledge Discovery in Databases (KDD) by Exploiting Parallelism in Generic KDD Primitives , 1997, NGITS.
[36] J. Muruzabal,et al. Fuzzy and probabilistic reasoning in simple learning classifier systems , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.
[37] H. Mühlenbein,et al. From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.
[38] Paul A. Viola,et al. MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.
[39] J. Tew,et al. Prolonged antigen half-life in the lymphoid follicles of specifically immunized mice. , 1979, Immunology.
[40] Bojan Cestnik,et al. Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.
[41] Domenico Talia,et al. Scalable Parallel Clustering for Data Mining on Multicomputers , 2000, IPDPS Workshops.
[42] Srinivasan Parthasarathy,et al. New Algorithms for Fast Discovery of Association Rules , 1997, KDD.
[43] Mark J. Willis,et al. Steady-state modelling of chemical process systems using genetic programming , 1997 .
[44] G. Harik. Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .
[45] John E. Hunt,et al. Recognising Promoter Sequences Using An Artificial Immune System , 1995, ISMB.
[46] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[47] S. Forrest,et al. Antibody repertoires and pathogen recognition: the role of germline diversity and somatic hypermutation , 1999 .
[48] R. Gershon,et al. "Clonal selection and after," and after. , 1979, The New England journal of medicine.
[49] LiMin Fu,et al. Neural networks in computer intelligence , 1994 .
[50] Bernard Widrow,et al. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[51] Vipin Kumar,et al. Scalable parallel data mining for association rules , 1997, SIGMOD '97.
[52] T. Kepler,et al. Somatic hypermutation in B cells: an optimal control treatment. , 1993, Journal of theoretical biology.
[53] Kazuto Kubota,et al. Parallelization of decision tree algorithm and its performance evaluation , 2000, Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region.
[54] Leandro Nunes de Castro,et al. The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .
[55] Sankar K. Pal,et al. Fuzzy models for pattern recognition : methods that search for structures in data , 1992 .
[56] P. Legendre,et al. Comparison tests for dendrograms: A comparative evaluation , 1995 .
[57] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[58] Lashon B. Booker,et al. Triggered Rule Discovery in Classifier Systems , 1989, ICGA.
[59] Timothy F. Cootes,et al. Object Recognition by Flexible Template Matching using Genetic Algorithms , 1992, ECCV.
[60] Ivan Bratko,et al. Trading Accuracy for Simplicity in Decision Trees , 1994, Machine Learning.
[61] Pat Langley,et al. Induction of Selective Bayesian Classifiers , 1994, UAI.
[62] John Hunt,et al. Augmenting an artificial immune network , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).
[63] J. Duffy,et al. Using Symbolic Regression to Infer Strategies from Experimental Data , 2002 .
[64] Jorma Rissanen,et al. SLIQ: A Fast Scalable Classifier for Data Mining , 1996, EDBT.
[65] Jon Timmis,et al. Jisys: The Envelopment of an Artificial Immune System for Real World Applications , 1999 .
[66] John H. Holland,et al. CONCERNING THE EMERGENCE OF TAG-MEDIATED LOOKAHEAD , 1990 .
[67] Dirk Thierens,et al. Mixing in Genetic Algorithms , 1993, ICGA.
[68] Sholom M. Weiss,et al. Predictive data mining - a practical guide , 1997 .
[69] Masaru Kitsuregawa,et al. Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach , 1998, PAKDD.
[70] David E. Goldberg,et al. Probability matching, the magnitude of reinforcement, and classifier system bidding , 2004, Machine Learning.
[71] John E. Hunt,et al. An adaptive, distributed learning system based on the immune system , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.
[72] Anil K. Jain,et al. 39 Dimensionality and sample size considerations in pattern recognition practice , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.
[73] Max Henrion,et al. Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.
[74] D. Ballard,et al. Complexity Drift in Evolutionary Computation with Tree Representations , 1996 .
[75] David Heckerman,et al. Bayesian Networks for Knowledge Discovery , 1996, Advances in Knowledge Discovery and Data Mining.
[76] Jon Timmis. On Parameter Adjustment of the Immune Inspired Machine Learning Algorithm AINE , 2000 .
[77] D. Fogel. Evolutionary algorithms in theory and practice , 1997, Complex..
[78] Yang Xiang,et al. Parallel Learning of Belief Networks in Large and Difficult Domains , 2004, Data Mining and Knowledge Discovery.
[79] Sunil Choenni. On the Suitability of Genetic-Based Algorithms for Data Mining , 1998, ER Workshops.
[80] L. Spector,et al. Quantum circuits for OR and AND of ORs , 2000 .
[81] Hugues Bersini,et al. Hints for Adaptive Problem Solving Gleaned from Immune Networks , 1990, PPSN.
[82] F. von Zuben,et al. An evolutionary immune network for data clustering , 2000, Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks.
[83] Dimitrios Gunopulos,et al. Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.
[84] Philip S. Yu,et al. Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..
[85] Anil K. Jain,et al. Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[86] N. K. Jerne,et al. Clonal selection in a lymphocyte network. , 1974, Society of General Physiologists series.
[87] Wray L. Buntine,et al. Graphical models for discovering knowledge , 1996, KDD 1996.
[88] Patrick Valduriez,et al. Parallel database systems: The case for shared-something , 1993, Proceedings of IEEE 9th International Conference on Data Engineering.
[89] Lawrence Hubert,et al. Graph-theoretic representations for proximity matrices through strongly-anti-Robinson or circular strongly-anti-Robinson matrices , 1998 .
[90] Stewart W. Wilson. Generalization in the XCS Classifier System , 1998 .
[91] Jerne Nk. Towards a network theory of the immune system. , 1974 .
[92] Ramakrishnan Srikant,et al. Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.
[93] David A. Patterson,et al. Computer Organization & Design: The Hardware/Software Interface , 1993 .
[94] Kenneth A. De Jong,et al. Using genetic algorithms for concept learning , 1993, Machine Learning.
[95] Yoshiki Uchikawa,et al. Moderationism in the immune system: gait acquisition of a legged robot using the metadynamics function , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).
[96] Peter W.H. Smith,et al. Genetic Programming as a Data-Mining Tool , 2002 .
[97] Peter Nordin,et al. Acquiring Textual Relations Automatically on the Web Using Genetic Programming , 2000, EuroGP.
[98] Sholom M. Weiss,et al. Computer Systems That Learn , 1990 .
[99] Mineichi Kudo,et al. Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..
[100] L. Altenberg. The evolution of evolvability in genetic programming , 1994 .
[101] Pier Luca Lanzi,et al. An Analysis of Generalization in the XCS Classifier System , 1999, Evolutionary Computation.
[102] Sharma Chakravarthy,et al. Performance Evaluation and Optimization of Join Queries for Association Rule Mining , 1999, DaWaK.
[103] Ethem Alpayddn,et al. Combined 5x2cv F Test for Comparing Supervised Classification Learning Algorithms Combined 5x2cv F Test for Comparing Supervised Classification Learning Algorithms , 1998 .
[104] Peter W. H. Smith. Controlling Code Growth in Genetic Programming , 1999 .
[105] Ron Kohavi,et al. Error-Based and Entropy-Based Discretization of Continuous Features , 1996, KDD.
[106] D. Goldberg,et al. BOA: the Bayesian optimization algorithm , 1999 .
[107] H. Chipman,et al. Bayesian CART Model Search , 1998 .
[108] Nir Friedman,et al. Tissue classification with gene expression profiles. , 2000 .
[109] David W. Aha,et al. A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.
[110] Jonathan Timmis,et al. Artificial Immune Systems : Using the Immune System as Inspiration for Data Mining , 2001 .
[111] Ryszard S. Michalski,et al. The AQ15 Inductive Learning System: An Overview and Experiments , 1986 .
[112] Masaru Kitsuregawa,et al. Parallel mining algorithms for generalized association rules with classification hierarchy , 1997, SIGMOD '98.
[113] H.,et al. The Immune System as a Model for Pattern Recognition and Classification , 1999 .
[114] G. W. Milligan,et al. An examination of procedures for determining the number of clusters in a data set , 1985 .
[115] Hitoshi Iba,et al. Genetic programming using a minimum description length principle , 1994 .
[116] Mohammed J. Zaki,et al. Parallel classification for data mining on shared-memory multiprocessors , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).
[117] Gregory Piatetsky-Shapiro,et al. The interestingness of deviations , 1994 .
[118] Keinosuke Fukunaga,et al. A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.
[119] Gilbert Syswerda,et al. Simulated Crossover in Genetic Algorithms , 1992, FOGA.
[120] Derek J. Smith,et al. Immunological Memory is Associative , 1998 .
[121] M. Dorigo,et al. Aco Algorithms for the Traveling Salesman Problem , 1999 .
[122] Heitor Silvério Lopes,et al. AN EVOLUTIONARY APPROACH TO SIMULATE COGNITIVE FEEDBACK LEARNING IN MEDICAL DOMAIN , 1997 .
[123] Ehl Emile Aarts,et al. Simulated annealing and Boltzmann machines , 2003 .
[124] Clark F. Olson,et al. Parallel Algorithms for Hierarchical Clustering , 1995, Parallel Comput..
[125] Lorenza Saitta,et al. Learning Disjunctive Concepts by Means of Genetic Algorithms , 1994, ICML.
[126] John H. Holland,et al. Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .
[127] Hans Henrik Thodberg,et al. Improving Generalization of Neural Networks Through Pruning , 1991, Int. J. Neural Syst..
[128] Prabhat Hajela,et al. Immune network modelling in design optimization , 1999 .
[129] D. Hand,et al. Bayesian partition modelling , 2002 .
[130] David J. Spiegelhalter,et al. Machine Learning, Neural and Statistical Classification , 2009 .
[131] J. R. Koehler,et al. Modern Applied Statistics with S-Plus. , 1996 .
[132] Jack Sklansky,et al. On Automatic Feature Selection , 1988, Int. J. Pattern Recognit. Artif. Intell..
[133] Bernd Fritzke,et al. Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.
[134] P. Ross,et al. An adverse interaction between crossover and restricted tree depth in genetic programming , 1996 .
[135] Rajeev Motwani,et al. Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.
[136] Alan S. Perelson,et al. The immune system, adaptation, and machine learning , 1986 .
[137] David B. Skillicorn,et al. Strategies for parallel data mining , 1999, IEEE Concurr..
[138] David W. Aha,et al. Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison , 1994 .
[139] Padhraic Smyth,et al. From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.
[140] P. Smith,et al. Code growth, explicitly defined introns, and alternative selection schemes. , 1998, Evolutionary computation.
[141] Alex A. Freitas,et al. A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction , 1997 .
[142] A Coutinho,et al. The self-nonself discrimination and the nature and acquisition of the antibody repertoire. , 1980, Annales d'immunologie.
[143] R. Phipps,et al. The Maintenance and Regulation of the Humoral Immune Response: Persisting Antigen and the Role of Follicular Antigen‐Binding Dendritic Cells as Accessory Cells , 1980, Immunological reviews.
[144] David Wai-Lok Cheung,et al. Effect of Data Skewness in Parallel Mining of Association Rules , 1998, PAKDD.
[145] Charles T. Zahn,et al. Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters , 1971, IEEE Transactions on Computers.
[146] Alberto Muñoz,et al. Diffuse Pattern Learning with Fuzzy ARTMAP and PASS , 1994, PPSN.
[147] Jude W. Shavlik,et al. Growing Simpler Decision Trees to Facilitate Knowledge Discovery , 1996, KDD.
[148] Ramakrishnan Srikant,et al. Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.
[149] Jiawei Han,et al. Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.
[150] Leandro Nunes de Castro,et al. An Immunological Approach to Initialize Feedforward Neural Network Weights , 2001 .
[151] S. Baluja,et al. Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .
[152] William B. Langdon,et al. Evolving Receiver Operating Characteristics for Data Fusion , 2001, EuroGP.
[153] David Wai-Lok Cheung,et al. Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors , 1998, SPAA '98.
[154] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[155] David Taniar,et al. Parallel Data Mining , 2002 .
[156] J Timmis,et al. An artificial immune system for data analysis. , 2000, Bio Systems.
[157] Jonathan Timmis. Artificial immune systems : a novel data analysis technique inspired by the immune network theory , 2000 .
[158] Ramakrishnan Srikant,et al. Fast algorithms for mining association rules , 1998, VLDB 1998.
[159] M. Pelikán,et al. The Bivariate Marginal Distribution Algorithm , 1999 .
[160] Pat Langley,et al. Models of Incremental Concept Formation , 1990, Artif. Intell..
[161] J. Carroll. “Minimax length links” of a dissimilarity matrix and minimum spanning trees , 1995 .
[162] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[163] J. Kittler,et al. Feature Set Search Alborithms , 1978 .
[164] Teuvo Kohonen,et al. Self-Organizing Maps , 2010 .
[165] Jude W. Shavlik,et al. Using Sampling and Queries to Extract Rules from Trained Neural Networks , 1994, ICML.
[166] F. Burnet. The clonal selection theory of acquired immunity , 1959 .
[167] Kwee-Bo Sim,et al. Artificial immune network-based cooperative control in collective autonomous mobile robots , 1997, Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI.
[168] A. Martin V. Butz,et al. The anticipatory classifier system and genetic generalization , 2002, Natural Computing.
[169] David Wai-Lok Cheung,et al. Efficient Mining of Association Rules in Distributed Databases , 1996, IEEE Trans. Knowl. Data Eng..
[170] Byoung-Tak Zhang,et al. Bayesian Methods for Efficient Genetic Programming , 2000, Genetic Programming and Evolvable Machines.
[171] Tian Zhang,et al. BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.
[172] Pedro Larrañaga,et al. The Convergence Behavior of the PBIL Algorithm: A Preliminary Approach , 2001 .
[173] Hugues Bersini,et al. The Immune Learning Mechanisms: Recruitment Reinforcement and their applications , 1993 .
[174] Leandro Nunes de Castro,et al. Artificial Immune Systems: Part I-Basic Theory and Applications , 1999 .
[175] David Wai-Lok Cheung,et al. An Adaptive Algorithm for Mining Association Rules on Shared-Memory Parallel Machines , 2001, Distributed and Parallel Databases.
[176] Stewart W. Wilson. Mining Oblique Data with XCS , 2000, IWLCS.
[177] Alex A. Freitas,et al. Data Mining with Evolutionary Algorithms: Research Directions , 2000 .
[178] Volker Tresp,et al. The generalized Bayesian committee machine , 2000, KDD '00.
[179] Jason Catlett,et al. Overprvning Large Decision Trees , 1991, IJCAI.
[180] Heinz Mühlenbein,et al. The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.
[181] Mohammed J. Zaki. Efficient enumeration of frequent sequences , 1998, CIKM '98.
[182] Mohammed J. Zaki. Parallel Sequence Mining on Shared-Memory Machines , 1999, Large-Scale Parallel Data Mining.
[183] Alexandre Parodi,et al. A New Approach to Fuzzy Classifier Systems , 1993, ICGA.
[184] John Kilcullen. William of Ockham: A Short Discourse on Tyrannical Government , 1992 .
[185] Gilbert Syswerda,et al. Uniform Crossover in Genetic Algorithms , 1989, ICGA.
[186] Pedro Larrañaga,et al. Feature Subset Selection by Bayesian network-based optimization , 2000, Artif. Intell..
[187] J. van Leeuwen,et al. Learning Classifier Systems , 2000, Lecture Notes in Computer Science.
[188] Srinivasan Parthasarathy,et al. Parallel Algorithms for Discovery of Association Rules , 1997, Data Mining and Knowledge Discovery.
[189] Henry Tirri,et al. Unsupervised Bayesian visualization of high-dimensional data , 2000, KDD '00.
[190] Bruno Leclerc,et al. Minimum spanning trees for tree metrics: abridgements and adjustments , 1995 .
[191] G. Oster,et al. Theoretical studies of clonal selection: minimal antibody repertoire size and reliability of self-non-self discrimination. , 1979, Journal of theoretical biology.
[192] Jano I. van Hemert,et al. Adapting the Fitness Function in GP for Data Mining , 1999, EuroGP.
[193] Tomasz Imielinski,et al. An Interval Classifier for Database Mining Applications , 1992, VLDB.
[194] Riccardo Poli,et al. Fitness Causes Bloat , 1998 .
[195] Peter Nordin,et al. Complexity Compression and Evolution , 1995, ICGA.
[196] Sanjay Ranka,et al. CLOUDS: A Decision Tree Classifier for Large Datasets , 1998, KDD.
[197] J. Ross Quinlan,et al. Generating Production Rules from Decision Trees , 1987, IJCAI.
[198] Terence Soule,et al. Code growth in genetic programming , 1996 .
[199] Sudipto Guha,et al. CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.
[200] Kenneth DeJong,et al. Robust feature selection algorithms , 1993, Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93).
[201] Nir Friedman,et al. On the Sample Complexity of Learning Bayesian Networks , 1996, UAI.
[202] M. A. Kaboudan,et al. Genetic evolution of regression models for business and economic forecasting , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[203] Gregory Piatetsky-Shapiro,et al. Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.
[204] J. Hartigan. REPRESENTATION OF SIMILARITY MATRICES BY TREES , 1967 .
[205] Stewart W. Wilson. Classifier systems and the animat problem , 2004, Machine Learning.
[206] Peter C. Cheeseman,et al. Bayesian Classification (AutoClass): Theory and Results , 1996, Advances in Knowledge Discovery and Data Mining.
[207] Roald Hoffmann,et al. Ockham's Razor and Chemistry * , 1997 .
[208] James E. Baker,et al. Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.
[209] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[210] Yin-Fu Huang,et al. Mining generalized association rules using pruning techniques , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[211] Giandomenico Spezzano,et al. Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees , 2000, EuroGP.
[212] Yiming Yang,et al. A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.
[213] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[214] BayesiannetworksPedro,et al. Combinatorial optimization by learning and simulation of , 2000 .
[215] Alex A. Freitas,et al. A Survey of Parallel Data Mining , 1996 .
[216] Alex A. Freitas,et al. A survey of evolutionary algorithms for data mining and knowledge discovery , 2003 .
[217] David J. Hand,et al. Construction and Assessment of Classification Rules , 1997 .
[218] Hiroshi Motoda,et al. Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.
[219] Philip S. Yu,et al. Paper Number : U 040 A Framework for Finding ProjectedClusters in High Dimensional , 2007 .
[220] Mohammed J. Zaki. Parallel and distributed association mining: a survey , 1999, IEEE Concurr..
[221] Yi Zhang,et al. Entropy-based subspace clustering for mining numerical data , 1999, KDD '99.
[222] Justin Doak,et al. An evaluation of feature selection methods and their application to computer security , 1992 .
[223] Peter Ross,et al. The evolution and analysis of potential antibody library for use in job-shop scheduling , 1999 .
[224] Pedro Larrañaga,et al. Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms , 2001, Int. J. Approx. Reason..
[225] Alex Alves Freitas,et al. Parallel Data Mining for Very Large Relational Databases , 1996, HPCN Europe.
[226] Jano I. van Hemert,et al. Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems , 2001, EuroGP.
[227] Andrew Y. Ng,et al. Preventing "Overfitting" of Cross-Validation Data , 1997, ICML.
[228] David J. DeWitt,et al. Parallel database systems: the future of high performance database systems , 1992, CACM.
[229] Josef Kittler,et al. Floating search methods in feature selection , 1994, Pattern Recognit. Lett..
[230] Byoung-Tak Zhang,et al. Balancing Accuracy and Parsimony in Genetic Programming , 1995, Evolutionary Computation.
[231] Rakesh Agrawal,et al. SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.
[232] Inderjit S. Dhillon,et al. A Data-Clustering Algorithm on Distributed Memory Multiprocessors , 1999, Large-Scale Parallel Data Mining.
[233] Abraham Silberschatz,et al. On Subjective Measures of Interestingness in Knowledge Discovery , 1995, KDD.