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PAKDD
2005
ACM
94views Data Mining» more  PAKDD 2005»
13 years 9 months ago
Progressive Sampling for Association Rules Based on Sampling Error Estimation
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. S...
Kun-Ta Chuang, Ming-Syan Chen, Wen-Chieh Yang
PAKDD
2005
ACM
63views Data Mining» more  PAKDD 2005»
13 years 9 months ago
Pruning Derivative Partial Rules During Impact Rule Discovery
Because exploratory rule discovery works with data that is only a sample of the phenomena to be investigated, some resulting rules may appear interesting only by chance. Techniques...
Shiying Huang, Geoffrey I. Webb
PAKDD
2005
ACM
124views Data Mining» more  PAKDD 2005»
13 years 9 months ago
Finding Sporadic Rules Using Apriori-Inverse
We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the w...
Yun Sing Koh, Nathan Rountree
PAKDD
2005
ACM
112views Data Mining» more  PAKDD 2005»
13 years 9 months ago
Approximated Clustering of Distributed High-Dimensional Data
In many modern application ranges high-dimensional feature vectors are used to model complex real-world objects. Often these objects reside on different local sites. In this paper,...
Hans-Peter Kriegel, Peter Kunath, Martin Pfeifle, ...
PAKDD
2005
ACM
142views Data Mining» more  PAKDD 2005»
13 years 9 months ago
Dynamic Cluster Formation Using Level Set Methods
Density-based clustering has the advantages for (i) allowing arbitrary shape of cluster and (ii) not requiring the number of clusters as input. However, when clusters touch each o...
Andy M. Yip, Chris H. Q. Ding, Tony F. Chan