Data Mining_ Concepts and Techniques - Jiawei Han [33]
Research results relating to data mining and data warehousing have been published in the proceedings of many international database conferences, including the ACM-SIGMOD International Conference on Management of Data (SIGMOD), the International Conference on Very Large Data Bases (VLDB), the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS), the International Conference on Data Engineering (ICDE), the International Conference on Extending Database Technology (EDBT), the International Conference on Database Theory (ICDT), the International Conference on Information and Knowledge Management (CIKM), the International Conference on Database and Expert Systems Applications (DEXA), and the International Symposium on Database Systems for Advanced Applications (DASFAA). Research in data mining is also published in major database journals, such as IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Database Systems (TODS), Information Systems, The VLDB Journal, Data and Knowledge Engineering, International Journal of Intelligent Information Systems (JIIS), and Knowledge and Information Systems (KAIS).
Many effective data mining methods have been developed by statisticians and introduced in a rich set of textbooks. An overview of classification from a statistical pattern recognition perspective can be found in Pattern Classification by Duda, Hart, and Stork [DHS01]. There are also many textbooks covering regression and other topics in statistical analysis, such as Mathematical Statistics: Basic Ideas and Selected Topics by Bickel and Doksum [BD01]; The Statistical Sleuth: A Course in Methods of Data Analysis by Ramsey and Schafer [RS01]; Applied Linear Statistical Models by Neter, Kutner, Nachtsheim, and Wasserman [NKNW96]; An Introduction to Generalized Linear Models by Dobson [Dob90]; Applied Statistical Time Series Analysis by Shumway [Shu88]; and Applied Multivariate Statistical Analysis by Johnson and Wichern [JW92].
Research in statistics is published in the proceedings of several major statistical conferences, including Joint Statistical Meetings, International Conference of the Royal Statistical Society and Symposium on the Interface: Computing Science and Statistics. Other sources of publication include the Journal of the Royal Statistical Society, The Annals of Statistics, the Journal of American Statistical Association, Technometrics, and Biometrika.
Textbooks and reference books on machine learning and pattern recognition include Machine Learning by Mitchell [Mit97]; Pattern Recognition and Machine Learning by Bishop [Bis06]; Pattern Recognition by Theodoridis and Koutroumbas [TK08]; Introduction to Machine Learning by Alpaydin [Alp11]; Probabilistic Graphical Models: Principles and Techniques by Koller and Friedman [KF09]; and Machine Learning: An Algorithmic Perspective by Marsland [Mar09]. For an edited collection of seminal articles on machine learning, see Machine Learning, An Artificial Intelligence Approach, Volumes 1 through 4, edited by Michalski et al. MCM83, MCM86, KM90 and MT94 and Readings in Machine Learning by Shavlik and Dietterich [SD90].
Machine learning and pattern recognition research is published in the proceedings of several major machine learning, artificial intelligence, and pattern recognition conferences, including the International Conference on Machine Learning (ML), the ACM Conference on Computational Learning Theory (COLT), the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Pattern Recognition (ICPR), the International Joint Conference on Artificial Intelligence (IJCAI), and the American Association of Artificial Intelligence Conference