IEEE Data Mining 2001: Final Call for Papers
ICDM '01: The 2001 IEEE International Conference on Data Mining Sponsored by the IEEE Computer Society -- Doubletree Hotel, San Jose, California, USA November 29 - December 2, 2001 Home Page: http://kais.mines.edu/~xwu/icdm/icdm-01.html INVITED SPEAKERS: Jerome H. Friedman, Stanford University, USA Jim Gray (The 1999 Turing Award Winner), Microsoft Research, USA Pat Langley, Daimler-Benz Research & Technology Center, USA Benjamin W. Wah (IEEE Computer Society President), UIUC, USA CORPORATE SPONSORS: Blue Martini Software, San Mateo, California; Insightful Corporation, Seattle, Washington; NARAX Inc., Golden, Colorado; Springer-Verlag, New York, New York; StatSoft Inc., Tulsa, Oklahoma Call for Papers *** ** - Papers due: June 15, 2001 - Submission website: http://wie.mines.edu/register/login.jsp - Electronic submissions are required in the form of PDF or postscript files. ** The 2001 IEEE International Conference on Data Mining (ICDM '01) provides a forum for the sharing of original research results and practical development experiences among researchers and application developers from different data mining related areas such as machine learning, automated scientific discovery, statistics, pattern recognition, knowledge acquisition, soft computing, databases and data warehousing, data visualization, and knowledge-based systems. The conference seeks solutions to challenging problems facing the development of data mining systems, and shapes future directions of research by promoting high quality, novel and daring research findings. As an important part of the conference, the workshops program will focus on new research challenges and initiatives. Topics of Interest == Topics related to the design, analysis and implementation of data mining theory, systems and applications are of interest. These include, but are not limited to the following areas: - Foundations and principles of data mining - Data mining algorithms and methods in traditional areas (such as classification, clustering, probabilistic modeling, and association analysis), and in new areas - Data and knowledge representation for data mining - Modeling of structured, textual, temporal, spatial, multimedia and Web data to support data mining - Complexity, efficiency, and scalability issues in data mining - Data pre-processing, data reduction, feature selection and feature transformation - Statistics and probability in large-scale data mining - Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining - Integration of data warehousing, OLAP and data mining - Man-machine interaction in data mining and visual data mining - Artificial intelligence contributions to data mining - High performance and distributed data mining - Machine learning, pattern recognition and automated scientific discovery - Quality assessment and interestingness metrics of data mining results - Process centric data mining and models of data mining process - Security and social impact of data mining - Emerging data mining applications, such as electronic commerce, Web mining and intelligent learning database systems Conference Publications and ICDM Best Paper Awards == High quality papers in all data mining areas are solicited. Papers exploring new directions will receive a careful and supportive review. All submitted papers should be limited to a maximum of 6,000 words (approximately 20 A4 pages), and will be reviewed on the basis of technical quality, relevance to data mining, originality, significance, and clarity. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. A selected number of ICDM '01 accepted papers will be expanded and revised for possible inclusion in the Knowledge and Information Systems journal (http://kais.mines.edu/~kais/) by Springer-Verlag. ICDM Best Paper Awards will be conferred on the authors of the best papers at the conference. Important Dates === June 15, 2001Paper submissions. July 31, 2001Acceptance notices. August 31, 2001 Final camera-readies. Nov 29 - Dec 2, 2001
Re: ATTN>>asian lover 40DD Satomi MPeg..soaping it up in the shower
Fuck!! It is a Virus!! Are you the crimer ,aren't you? Stop post the files include virus to Newsgroup server! = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
correction procedures
Hello, I am a general surgeon in a state hospital, Ankara, Turkey. I performed a study on " different enteral nutrients and bacterial translocation in experimental obstructive jaundice." There was 5 groups of rats. Each group consists of 20 rats. Occurred Translocation incidences in mesenteric lymph nodes were shown in following table. My aim was to compare groups 2, 3, 4 with control(group 1) Transloc.yes Transloc.no total Group 1 sham ligation of bile duct1 1920 (fed rat chow) Group 2 bile duct ligated 9* 1120 (fed rat chow) Group 3 bile duct ligated 8**1220 (fed enteral diet) Group 4 bile duct ligated 1 1920 (fed enteral diet 2) Group 5 bile duct ligated 1 1920 (fed enteral diet 3) *p=0.008 **p=0.02 By chi squared test I calculated this p values. The reviewer commented that I should do bonferroni correction, find adjusted p value and according to this adjusted value, I should say significant or not. However, in no study have I read that the authors had written that they had adjusted bonferroni correction, especially in a comparision by chi square test. If bonferroni was performed then adjusted p value would be 0.05/10=0.005, 10= nx(n-1) in our study. Thus our results would not be significant. Is it appropriate to make bonferroni correction or simes correction in this situation? Indeed I want to compare groups 2, 3, 4 with group 1. So there would be 4 comparison.Is simes procedure is correct? How can I make Simes correction? Can you help me ? Thank you Bekir Kuru [EMAIL PROTECTED] = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
mutual information "amif function in R"
Just a question for R porogramming language ( by Adrian Trapletti). I don't understand function "amif" and I need an interpretation of it. I would like to know if amif can be used both linear and nonlinear model, and what is the interval for parameters (surrogate, confidence, ci). I've already tryed to read the R's manuals, but it's not clear (for me). I' m studyng the nonlinear coefficient R based on mutual information proposed by Granger and Lin (1994) in "Using the mutual information coefficient to identify lags in nonlinear models" for my degree thesis. Thanks to all (please help me!!) Monica = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: correction procedures
On 2 Jun 2001, Bekir wrote in part: > I performed a study on " different enteral nutrients and bacterial > translocation in experimental obstructive jaundice." > > There was 5 groups of rats. Each group consists of 20 rats. Occurred > Translocation incidences in mesenteric lymph nodes were shown in > following table. My aim was to compare groups 2, 3, 4 with > control(group 1) < Data table deleted; see the original posting. > < Summary of group definitions and comparison results: > Group 1 sham ligation of bile duct (fed rat chow) > Group 2 bile duct ligated (fed rat chow) *p = 0.08 > Group 3 bile duct ligated (fed enteral diet) ** p = 0.02 > Group 4 bile duct ligated (fed enteral diet 2) > Group 5 bile duct ligated (fed enteral diet 3) > By chi squared test I calculated this p values. You did not specify, but presumably the chi-square test in question was of a series of 2x2 tables, comparing the numbers of translocations that occurred (vs. the numbers that didn't) in Group 1 (your control group) with each of the other groups. > The reviewer commented that I should do bonferroni correction, find > adjusted p value and according to this adjusted value, I should say > significant or not. However, in no study have I read that the authors > had written that they had adjusted bonferroni correction, especially > in a comparision by chi square test. The 1-degree-of-freedom chi-square test described above is exactly equivalent to a z-test comparing the proportion of translocations in Group 1 with the proportion of translocations in the other group, for each conmparison of interest. You may perhaps find references to Bonferroni adjustments in studies where z- or t-tests were used. > If bonferroni was performed then adjusted p value [Here you must mean the adjusted significance level alpha, not the p-value? -- DFB.] > would be 0.05/10=0.005, 10 = nx(n-1) in our study. I do not think so. The number of comparisons you say you were interested in is three, not ten: Group 2 vs. Group 1, Group 3 vs. Group 1, and Group 4 vs. Group 1. If indeed these are the only comparisons of interest, and in the sense that these comparisons (and no others!) were planned from the beginning, then the adjusted p-values would be 0.02*3 = 0.06 and 0.008*3 = 0.024. But I do not believe this, either. If these were the only three comparisons of interest, you would not have bothered to include Group 5 in the experiment. It looks to me as though the original design had envisioned comparisons of Groups 2, 3, 4, 5 vs. Group 1, and may also have intended comparisons of Groups 3, 4, 5 vs. Group 2; so that the number of comparisons for the Bonferroni correction would be either 4, or 4+3 = 7. The corresponding adjusted p-values would be 0.02*4 = 0.08 and 0.008*4 = 0.032; or 0.02*7 = 0.14 and 0.008*7 = 0.056. > Thus our results would not be significant. > Is it appropriate to make bonferroni correction or simes correction in > this situation? > Indeed I want to compare groups 2, 3, 4 with group 1. So there would > be 4 comparisons. Then you must mean "compare groups 2, 3, 4, 5 with group 1"? > Is simes procedure is correct? How can I make Simes correction? Sorry, I'm not familiar with this procedure, at least not by that name. I hope this has been helpful. Donald F. Burrill [EMAIL PROTECTED] 348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED] MSC #29, Plymouth, NH 03264 603-535-2597 184 Nashua Road, Bedford, NH 03110 603-471-7128 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: ANOVA and regression
Consider: Introducing ANOVA and ANCOVA-a GLM approach, by Adrew Rutherford, Sage Publications 2001 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
