77 search results - page 2 / 16 » On the Anonymization of Sparse High-Dimensional Data |
PAKDD
2009
ACM
14 years 1 days ago
2009
ACM
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To addre...
DEXA
2009
Springer
13 years 12 months ago
2009
Springer
Abstract. In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector ...
ICANN
2009
Springer
13 years 10 months ago
2009
Springer
Abstract. Many applications of machine learning involve sparse highdimensional data, where the number of input features is (much) larger than the number of data samples, d n. Predi...
SIGMOD
2001
ACM
14 years 5 months ago
2001
ACM
The outlier detection problem has important applications in the eld of fraud detection, network robustness analysis, and intrusion detection. Most such applications are high dimen...
ICDM
2002
IEEE
13 years 10 months ago
2002
IEEE
The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...