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KDD
1999
ACM
206views Data Mining» more  KDD 1999»
9 years 3 months ago
Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions
Efficiently answering decision support queries is an important problem. Most of the work in this direction has been in the context of the data cube. Queries are efficiently answer...
Jayavel Shanmugasundaram, Usama M. Fayyad, Paul S....
KDD
1999
ACM
142views Data Mining» more  KDD 1999»
9 years 3 months ago
Mining GPS Data to Augment Road Models
Many advanced safety and navigation applications in vehicles require accurate, detailed digital maps, but manual lane measurements are expensive and time-consuming, making automat...
Seth Rogers, Pat Langley, Christopher Wilson
KDD
1999
ACM
145views Data Mining» more  KDD 1999»
9 years 3 months ago
Discovery of Fraud Rules for Telecommunications - Challenges and Solutions
Many fraud analysis systemshave at their heart a rule-based enginefor generatingalertsaboutsuspiciousbehaviors.The rules in the systemareusually basedon expert knowledge. Automati...
Saharon Rosset, Uzi Murad, Einat Neumann, Yizhak I...
KDD
1999
ACM
150views Data Mining» more  KDD 1999»
9 years 3 months ago
Active Mining in a Distributed Setting
Srinivasan Parthasarathy, Sandhya Dwarkadas, Mitsu...
KDD
1999
ACM
117views Data Mining» more  KDD 1999»
9 years 3 months ago
Identifying Distinctive Subsequences in Multivariate Time Series by Clustering
Most time series comparison algorithms attempt to discover what the members of a set of time series have in common. We investigate a di erent problem, determining what distinguish...
Tim Oates
KDD
1999
ACM
104views Data Mining» more  KDD 1999»
9 years 3 months ago
Learning Rules from Distributed Data
In this paper a concern about the accuracy (as a function of parallelism) of a certain class of distributed learning algorithms is raised, and one proposed improvement is illustrat...
Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowye...
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
9 years 3 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
KDD
1999
ACM
166views Data Mining» more  KDD 1999»
9 years 3 months ago
CACTUS - Clustering Categorical Data Using Summaries
Clustering is an important data mining problem. Most of the earlier work on clustering focussed on numeric attributes which have a natural ordering on their attribute values. Rece...
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishn...
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