SIGKDD Explorations 1999

Conference Reports

Behind-the-Scenes Data Mining   by  George H. John
(available in PDF and Postscript formats)
ABSTRACT:Successful technology becomes invisible.  Few people think much about internal combustion engines while they drive to work in three-thousand-pound hunks of metal powered by them, or electricity while it enables countless parts of their modern lives.  Data mining has a long way to go before it succeeds in this way — or does it?  At KDD-98, the Behind-the-Scenes Data Mining panel presented five views on this aspect of the success of data mining. George H. John, Data Mining Guru at E.piphany, moderated the panel which included Usama Fayyad, Senior Researcher at Microsoft; Elliot Fishman, Director of Product Management at DoubleClick; Gerald Fahner, Project Analyst at Fair Isaac & Co.; and Paul DuBose, CTO of Analytika.  The story below is a fictional amalgam of their presentations, seen from the viewpoint of an average citizen — a day in the life of John Q. Record.
Keywords: Data Mining, Applications, KDD-98 Panel Discussion Report, Embedded data mining applications.

KDD-98 Exhibits Report by  Ismail Parsa
(available in PDF and Postscript formats)
ABSTRACT: Initially started as a half-day long display of research prototypes, the KDD exhibits quickly evolved into an industry event eagerly sought by the practitioners, researchers and the vendors in the field. In this article, I provide an overview of the KDD-98 exhibits program and the outline the factors that led to its success.
Keywords: KDD attendance statistics, KDD exhibits and KDD exhibit talks.

KDD at Interface-98   by  Arnold Goodman
(available in PDF and Postscript formats)

The Interface?98 Conference – a Resource for KDD   by   John Elder
(available in PDF and Postscript formats)
ABSTRACT: The annual Symposium on the Interface between Computer Science and Statistics is the statistical conference likely to be of greatest interest to quantitative practitioners of KDD.
Keywords: Data Mining, Statistics

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SIGKDD NEWSLETTER CONTACT INFORMATION:

Usama Fayyad
Microsoft Research
One Microsoft Way
Redmond, WA 98052

Fax: +1-425-936-7329
fayyad@acm.org

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SIGKDD Explorations

Statistics and Data Mining: Intersecting Disciplines   by    David J. Hand
(available in PDF and Postscript formats)
ABSTRACT: Statistics and data mining have much in common, but they also have differences. The nature of the two disciplines is examined, with emphasis on their similarities and differences.
Keywords: Statistics, data mining, knowledge discovery.

A Survey of Data Mining software Tools
by Michael Goebel and Le Gruenwald
(available in PDF and Postscript formats)
ABSTRACT: Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. We propose a feature classification scheme that can be used to study knowledge and data mining software. This scheme is based on the software’s general characteristics, database connectivity, and data mining characteristics. We then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. Finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either not addressed or insufficiently solved yet.
Keywords: Knowledge discovery in databases, data mining, surveys.

A Bibliography of Temporal, Spatial, and Spatio-Temporal Data Mining Research    by    J.F. Roddick and M. Spiliopoulou
(available in PDF and Postscript formats)
ABSTRACT: With the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry.At the same time, a greater recognition of the value of temporal and spatial data has been evident and the first papers looking at the confluence of these two areas are starting to emerge.This short paper provides a few comments on this research and provides a bibliography of relevant research papers investigating temporal, spatial and spatio-temporal data mining.
Keywords: Temporal Data Mining, Temporal Rules, Temporal Patterns, Spatial Data Mining, Spatio-Temporal Data Mining.

Mining Sales Data using a Neural Network Model of Market Response
by   T. S. Gruca, B. R. Klemz, and E. A. Furr Petersen
(available in PDF and Postscript formats)
ABSTRACT: Modeling aggregate market response is a core issue in marketing research.  In this research, we extend previous forecasting comparative research by comparing the forecasting accuracy of feed-forward neural network models to the premier market modeling technique, Multiplicative Competitive Interaction (MCI) models. Forecasts are compared in two separate studies: (1) the Information Resources Inc. (IRI) coffee dataset from Marion, IN and (2) the A.C. Nielsen catsup dataset from Sioux Falls, SD. Our results suggest neural networks are a useful substitute for MCI models when there are too few observations available to estimate a fully-extended MCI model. Implications are discussed.
Keywords: neural networks, market response model, sales/market share forecasting

Book Reviews

Discovering Data Mining: From Concept to Implementation
by  Karim K. Hirji
(available in PDF and Postscript formats)
ABSTRACT: This paper is a review of the book Discovering Data Mining: From Concept to Implementation – Peter Cabena, Pablo Hadjinian, Rolf Stadler, Jaap Verhees, and Alessandro Zanasi (New Jersey: Prentice Hall, 195 pp., 1998).
Keywords: Data mining, Book review.

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