ACM Special Interest Group on Knowledge Discovery & Data Mining

KDD-2000

Sixth ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining
August 20-23, 2000
Boston, MA, USA

 KDD-2000 Accepted Industrial Track Papers

• List of Accepted Full Papers

• List of Accepted Reserve Papers

Note: if all authors have the same affiliation, the affiliation is listed following the last author.  Papers are ordered by their ID number.

Accepted Full Papers

Hybrid Poisson Process (#103).

A. Farahat (HNC Software).

Data Mining to Detect Abnormal Behavior in Aerospace Data (#105).

J. M. Pe�a (DLSIIS, Facultad de Inform�tica, UPM) , F. Famili (Institute for Information Technology, National Research Council, Ottawa), and S. L�tourneau (Institute for Information Technology, National Research Council, Ottawa).

Automating Exploratory Data Analysis for Efficient Data Mining (#114).

J. Becher, P. Berkhin, and E. Freeman (Accrue Software).

Predictive Modeling in Automotive Direct Marketing: Tools, Experiences and Open Issues (#116).

R. Wirth, W. Gersten, and D. Arndt (DaimlerChrysler).

Evolutionary Algorithms in Data Mining:  Multi-Objective Performance Modeling for Direct Marketing (#118).

S. Bhattacharyya (University of Illinois at Chicago).

Identifying Prospective Customers (#120).

P. Chou (IBM T.J. Watson Research Center), E. Grossman (IBM Global Business Intelligence Solutions), D. Gunopulos (University of California, Riverside), and P. Kamesam (IBM Insurance Research Center).

Textual Data Mining of Service Center Call Records (#125).

P.-N. Tan (University of Minnesota), H. Blau (Honeywell Technology Center), S. Harp (Honeywell Technology Center), and R. Goldman (Honeywell Technology Center).

Cross-Sell:  A Fast Promotion-Tunable Customer-Item Recommendation Method Based on Conditionally Independent Probabilities (#126).

B. Kitts, D. Freed, and J. Kommers (Vignette).

Genome Scale Prediction of Protein Functional Class from Sequence using Data Mining (#131).

R. D. King (University of Wales, Aberystwyth), A. Karwath (University of Wales, Aberystwyth), A. Clare (University of Wales, Aberystwyth), and L. Dehaspe (Katholieke Universiteit Leuven).

Targeting the Right Students Using Data Mining (#132).

Y. Ma (National University of Singapore), B. Liu (National University of Singapore), C.-K. Wong (National University of Singapore), P. Yu (IBM T. J. Watson Research Center), and S. M. Lee (Gifted Education Branch, Ministry of Education, Singapore). 

DEFECTION DETECTION:  Using Online Activity Profiles to Predict ISP Customer Vulnerability (#135).

N. Raghavan (AT&T Labs Research), R. Bell (AT&T Labs Research), M. Schonlau (RAND), D. Pregibon (AT&T Labs Research), and A. Karr (National Institute of Statistical Sciences).

Agglomerative Clustering of a Search Engine Query Log (#138).

D. Beeferman (Lycos) and A. Berger (Carnegie Mellon University).

Data Mining Solves Tough Semiconductor Manufacturing Problems (#153).

R. Gardner and J. Bieker (Motorola).

Mining the Stock Market:  Which Measure Is Best? (#221).

M. Gavrilov, D. Anguelov, P. Indyk, and R. Motwani (Stanford University).

Discovering Similar Patterns in Time Series (#259). 

J. P. Caraca-Valente, and I. Lopez-Chavarrias (Universidad Politecnica Madrid).

Data Mining Techniques for Optimizing Inventories for Electronic Commerce (#315).

A. Dhond, A. Gupta, and S. Vadhavkar (Massachusetts Institute of Technology).

Accepted Reserve Papers

Classification of Voice/Data/Fax Traffic Using Sampling, Data Mining and Market Research (#111).

G. Ma, T. Au, and F. Li (AT&T).

Discovery of Multi-Level Rules and Exceptions from  (#129).

R. P�irc�ir, S. McClean, and B. Scotney (University of Ulster).

Exploration Mining in Diabetic Patients Databases:  Findings and Conclusions (#136).

W. Hsu, M. Lee, B. Liu, and T. Ling (National University of Singapore).

Incremental Quantile Estimation for Massive Tracking (#137).

F. Chen (Bell Labs, Lucent Technologies), D. Lambert (Bell Labs, Lucent Technologies), and J. Pinheiro (Bell Labs).

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