69 search results - page 3 / 14 » Parallel Mining of Outliers in Large Database |
SBACPAD
2003
IEEE
13 years 10 months ago
2003
IEEE
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of d...
KDD
2008
ACM
14 years 5 months ago
2008
ACM
Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set. All...
SIGMOD
1998
ACM
13 years 9 months ago
1998
ACM
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clust...
ICPADS
2006
IEEE
13 years 11 months ago
2006
IEEE
When computationally feasible, mining extremely large databases produces tremendously large numbers of frequent patterns. In many cases, it is impractical to mine those datasets d...
EUROPAR
1999
Springer
13 years 9 months ago
1999
Springer
This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We ...