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...
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...
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...
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, ...
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...