The Handbook of Data Mining

Contents: G. Salvendy, Foreword. N. Ye, Preface. Part I:Methodologies of Data Mining. J. Gehrke, Decision Trees. G.I. Webb, Association Rules. J. Si, B.J. Nelson, G.C. Runger, Artificial Neural Network Models for Data Mining. C.M. Borror, Statistical Analysis of Normal and Abnormal Data. D. Madigan, G. Ridgeway, Bayesian Data Analysis. S.L. Scott, Hidden Markov Processes and Sequential Pattern Mining. G. Ridgeway, Strategies and Methods for Prediction. D.W. Apley, Principal Components and Factor Analysis. E. Ip, I. Cadez, P. Smyth, Psychometric Methods of Latent Variable Modeling. J. Ghosh, Scalable Clustering. G. Das, D. Gunopulos, Time Series Similarity and Indexing. Y-C. Lai, Z. Liu, N. Ye, T. Yalcinkaya, Nonlinear Time Series Analysis. B-H. Park, H. Kargupta, Distributed Data Mining. Part II:Management of Data Mining. D. Pyle, Data Collection, Preparation, Quality, and Visualization. T. Wu, X. Li, Data Storage and Management. H. Liu, L. Yu, H. Motoda, Feature Extraction, Selection, and Construction. S.M. Weiss, T. Zhang, Performance Analysis and Evaluation. C. Clifton, Security and Privacy. R. Grossman, M. Hornick, G. Meyer, Emerging Standards and Interfaces. Part III:Applications of Data Mining. D.A. Nembhard, Mining Human Performance Data. R. Feldman, Mining Text Data. S. Shekhar, R.R. Vatsavai, Mining Geospatial Data. C. Kamath, Mining Science and Engineering Data. M.J. Zaki, Mining Data in Bioinformatics. R. Cooley, Mining Customer Relationship Management (CRM) Data. N. Ye, Mining Computer and Network Security Data. C. Djeraba, G. Fernandez, Mining Image Data. M.C. Testik, G.C. Runger, Mining Manufacturing Quality Data.

[1]  Bin Zhu,et al.  Creating a large-scale content-based airphoto image digital library , 2000, IEEE Trans. Image Process..

[2]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[3]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.

[4]  Christopher K. I. Williams.  Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[5]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[6]  David L. Dowe,et al.  MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions , 2000, Stat. Comput..

[7]  William DuMouchel,et al.  Empirical bayes screening for multi-item associations , 2001, KDD '01.

[8]  Michael I. Jordan,et al.  Bayesian parameter estimation via variational methods , 2000, Stat. Comput..

[9]  B. Carlin,et al.  Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .

[10]  Alex Alves Freitas,et al.  On rule interestingness measures , 1999, Knowl. Based Syst..

[11]  D. Madigan,et al.  Eliciting prior information to enhance the predictive performance of Bayesian graphical models , 1995 .

[12]  D. Hawkins.  A Cusum for a Scale Parameter , 1981 .

[13]  W. A. Wallis,et al.  Techniques of Statistical Analysis. , 1950 .

[14]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[15]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[16]  C. Robert.  The Metropolis–Hastings Algorithm , 2015, 1504.01896.

[17]  J. York,et al.  Bayesian Graphical Models for Discrete Data , 1995 .

[18]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[19]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[20]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[21]  Fei-Long Chen,et al.  A neural-network approach to recognize defect spatial pattern in semiconductor fabrication , 2000 .

[22]  B. De Finetti,et al.  Funzione caratteristica di un fenomeno aleatorio , 1929 .

[23]  Rajjan Shinghal,et al.  Evaluating the Interestingness of Characteristic Rules , 1996, KDD.

[24]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[25]  Peter Congdon.  Bayesian statistical modelling , 2002 .

[26]  A. Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[27]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory, Third Edition , 1989, Springer Series in Information Sciences.

[28]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[29]  Stephen V. Crowder,et al.  Design of Exponentially Weighted Moving Average Schemes , 1989 .

[30]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[31]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[32]  A. Justel,et al.  Heterogeneity and model uncertainty in bayesian regression models , 1998 .

[33]  C. Malsburg,et al.  How patterned neural connections can be set up by self-organization , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[34]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[35]  R. G. Davis,et al.  Knowledge discovery from supplier change control data for purchasing management , 2001, 2001 International Conferences on Info-Tech and Info-Net. Proceedings (Cat. No.01EX479).

[36]  David D. Lewis,et al.  Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval , 1998, ECML.

[37]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[38]  George C. Runger,et al.  Comparison of multivariate CUSUM charts , 1990 .

[39]  Nola D. Tracy,et al.  Decomposition of T2 for Multivariate Control Chart Interpretation , 1995 .

[40]  D. Madigan,et al.  Bayesian Model Averaging for Linear Regression Models , 1997 .

[41]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[42]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[43]  Geoffrey I. Webb.  Discovering associations with numeric variables , 2001, KDD '01.

[44]  Roberto J. Bayardo,et al.  Mining the most interesting rules , 1999, KDD '99.

[45]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[46]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[47]  James O. Berger,et al.  Bayesian Analysis: A Look at Today and Thoughts of Tomorrow , 2000 .

[48]  Sreerama K. Murthy,et al.  Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey , 1998, Data Mining and Knowledge Discovery.

[49]  C. Malsburg.  Self-organization of orientation sensitive cells in the striate cortex , 2004, Kybernetik.

[50]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[51]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[52]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[53]  William H. Woodall,et al.  THE STATISTICAL DESIGN OF CUSUM CHARTS , 1993 .

[54]  P. Green,et al.  On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .

[55]  Jorma Rissanen,et al.  MDL-Based Decision Tree Pruning , 1995, KDD.

[56]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[57]  R. Crosier.  Multivariate generalizations of cumulative sum quality-control schemes , 1988 .

[58]  Casimir A. Kulikowski,et al.  Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[59]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[60]  A B Schwartz,et al.  Direct cortical representation of drawing. , 1994, Science.

[61]  Jennie Si,et al.  Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike Signals , 1997, Neural Computation.

[62]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

[63]  Jerome H. Friedman,et al.  A Recursive Partitioning Decision Rule for Nonparametric Classification , 1977, IEEE Transactions on Computers.

[64]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[65]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[66]  Dimitrios Gunopulos,et al.  Constraint-Based Rule Mining in Large, Dense Databases , 2004, Data Mining and Knowledge Discovery.

[67]  Sigal Sahar,et al.  Interestingness via what is not interesting , 1999, KDD '99.

[68]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[69]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[70]  JOHANNES GEHRKE,et al.  RainForest—A Framework for Fast Decision Tree Construction of Large Datasets , 1998, Data Mining and Knowledge Discovery.

[71]  A. P. Dawid,et al.  Applications of a general propagation algorithm for probabilistic expert systems , 1992 .

[72]  Colin Campbell,et al.  Bayes Point Machines , 2001, J. Mach. Learn. Res..

[73]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[74]  Wynne Hsu,et al.  Analyzing the Subjective Interestingness of Association Rules , 2000, IEEE Intell. Syst..

[75]  Kimmo Hätönen,et al.  A computer host-based user anomaly detection system using the self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[76]  Fah Fatt Gan,et al.  An optimal design of CUSUM quality control charts , 1991 .

[77]  H. Hotelling,et al.  Multivariate Quality Control , 1947 .

[78]  Hongjun Lu,et al.  H-mine: hyper-structure mining of frequent patterns in large databases , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[79]  Glenn Shafer,et al.  Probabilistic expert systems , 1996, CBMS-NSF regional conference series in applied mathematics.

[80]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[81]  S. W. Roberts.  Control chart tests based on geometric moving averages , 2000 .

[82]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[83]  Yehuda Lindell,et al.  A Statistical Theory for Quantitative Association Rules , 1999, KDD.

[84]  Adrian F. M. Smith,et al.  A Bayesian CART algorithm , 1998 .

[85]  Jennie Si,et al.  The best approximation to C2 functions and its error bounds using regular-center Gaussian networks , 1994, IEEE Trans. Neural Networks.

[86]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[87]  George C. Runger,et al.  Designing a Multivariate EWMA Control Chart , 1997 .

[88]  Johannes Gehrke,et al.  BOAT—optimistic decision tree construction , 1999, SIGMOD '99.

[89]  Joseph G. Ibrahim,et al.  Bayesian Survival Analysis , 2004 .

[90]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[91]  Yasuhiko Morimoto,et al.  Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases , 1998, VLDB.

[92]  Mohammed J. Zaki,et al.  Efficiently mining maximal frequent itemsets , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[93]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[94]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[95]  David J. Spiegelhalter,et al.  Sequential updating of conditional probabilities on directed graphical structures , 1990, Networks.

[96]  Jeff Harrison,et al.  Applied Bayesian Forecasting and Time Series Analysis , 1994 .

[97]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[98]  Charles W. Champ,et al.  A multivariate exponentially weighted moving average control chart , 1992 .

[99]  Roy Rada,et al.  Machine learning - applications in expert systems and information retrieval , 1986, Ellis Horwood series in artificial intelligence.

[100]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[101]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[102]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[103]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[104]  James M. Lucas,et al.  Exponentially weighted moving average control schemes: Properties and enhancements , 1990 .

[105]  Connie M. Borror,et al.  Probability and Statistics for Engineering and the Sciences, 5th Ed. , 2002 .

[106]  G T Seaborg,et al.  Gross National Product. , 1957, Science.

[107]  M. J. Bayarri,et al.  Bayesian measures of surprise for outlier detection , 2003 .

[108]  A. Gelfand,et al.  Bayesian Model Choice: Asymptotics and Exact Calculations , 1994 .

[109]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[110]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[111]  Douglas C. Montgomery,et al.  A review of multivariate control charts , 1995 .

[112]  Hong Yan,et al.  Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM) , 1999, IEEE Trans. Neural Networks.