165 search results - page 20 / 33 » Adaptive dimension reduction for clustering high dimensional... |
129
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ICDE
2012
IEEE
13 years 2 months ago
2012
IEEE
—Outlier mining is a major task in data analysis. Outliers are objects that highly deviate from regular objects in their local neighborhood. Density-based outlier ranking methods...
AUSAI
2007
Springer
15 years 5 months ago
2007
Springer
Dimensional reduction may be effective in order to compress data without loss of essential information. Also, it may be useful in order to smooth data and reduce random noise. The...
NPL
1998
14 years 11 months ago
1998
Abstract. Incremental learning of sensorimotor transformations in high dimensional spaces is one of the basic prerequisites for the success of autonomous robot devices as well as b...
ICDM
2006
IEEE
15 years 5 months ago
2006
IEEE
High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to...
111
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KDD
2000
ACM
15 years 3 months ago
2000
ACM
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...