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1、顶级会议 第一 KDD 第二 SIAM ICDM 中国计算机学会推荐国际学术刊物 (数据库、数据挖掘与内容检索) 序号刊物简称刊物全称出版社网址 1TODSACM Transactions on Database SystemsACMhttp:/www.acm.org/tods/2TOISACM Transactions on Information and Systems ACMhttp:/www.acm.org/pubs/tois/3TKDEIEEE Transactions on Knowledge and Data EngineeringIEEE Computer Societyhtt
2、p:/puter.org/tkde/4VLDBJVLDB JournalSpringer-Verlag http:/www.vldb.org/dblp/db/journals/vldb/index.html二、B 类 序号刊物简称刊物全称出版社网址 1TKDDACM Transactions on Knowledge Discovery from DataACM http:/www.acm.org/pubs/tkdd/2AEI Advanced Engineering InformaticsElsevier http:/ cws_home/622240/3DKEData and Knowled
3、ge Engineering Elsevier http:/ 0169023X4DMKDData Mining and Knowledge DiscoverySpringer http:/ Journal of Information SystemsThe OR Societyhttp:/www.palgrave- 6 GeoInformaticaSpringer http:/ Information Processing and ManagementElsevier http:/ Information SciencesElsevier http:/ SystemsElsevier htt
4、p:/ systems/10JASISTJournal of the American Society for Information Science and Technology American Society for Information Science and Technology http:/www.asis.org/Publications/JASIS/jasis.html11JWSJournal of Web SemanticsElsevier http:/ Knowledge and Information SystemsSpringer http:/ TWEBACM Tra
5、nsactions on the WebACMhttp:/tweb.acm.org/三、C 类 序号刊物简称刊物全称出版社网址 1DPD Distributed and Parallel Databases Springer http:/ 杰出科学 家，IEEE Fellow，IAPR Fellow，中国计算机学会会士;特聘教授，国家杰出青年基金获得者。二、一些学习资源/主要是网站 1.Statistical Learning Theory from Berkeley This course will provide an introduction to probabilistic and c
6、omputational methods for the statistical modeling of complex, multivariate data. It will concentrate on graphical models, a flexible and powerful approach to capturing statistical dependencies in complex, multivariate data. In particular, the course will focus on the key theoretical and methodologic
7、al issues of representation, estimation, and inference. 2.Data Mining from Stanford This will also be helpful. 3.The Lasso Page（略有点 old） The Lasso is a shrinkage and selection method for linear regression. It minimizes the usual sum of squared errors, with a bound on the sum of the absolute values o
8、f the coefficients. It has connections to soft-thresholding of wavelet coefficients, forward stagewise regression, and boosting methods. 4.Data Mining Tutorials This is a really informative website with tutorials on statistical data mining. They were written by Andrew Moore an employee at Google. He
9、 covers the foundation of data analysis, including decision trees, Bayesian classifiers and many other techniques weve been learning in class. I great website to check out if youre having trouble with any topics or simply would just like to learn more. 5.Data Mining Research This is a comprehensive
10、blog about the latest developments in data mining research. Provides a great overview of what scholars and professionals are talking about with regards to the discipline. The individual who started this blog is a working professional in the field, working for FinScore, a Swiss provider of software a
11、nd professional services focusing in data mining and customer intelligence. A couple very interesting and insightful posts from the blog include: “10 Very Interesting People in Data Mining,” “Data Mining: A New Weapon in the Fight Against Medicaid Fraud,” and “Worst practices in Data Mining.” Stepha
12、nie Santoso 6.Statistical Learning Article An article on the elements on statistical learning, how data mining is used to give predictions. Azai Ighadaro7.Kernel-Machines.Org This page is devoted to learning methods building on kernels, such as the support vector machine. It grew out of earlier page
13、s at the Max Planck Institute for Biological Cybernetics and at GMD FIRST, snapshots of which can be found here and here. In those days, information about kernel methods was sparse and nontrivial to find, and the kernel machines web site acted as a central repository for the field. It included a lis
14、t of people working in the field, and online preprints of most publications. 8.Welcome to Boosting.org We are pleased to announce a new website on Boosting and related ensemble learning methods, e.g. Boosting, Arcing, Bagging, the connection to mathematical programming and large margin classifiers, and model selection. The aim is to serve as a central information source by providing links to papers, upcoming events, datasets, code, etc. 9.Perfectly Random Sampling with Markov Chains Random sampling has found numerous applications in physics, statistics, and computer science. Perhaps the most
