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» Scaling Clustering Algorithms to Large Databases
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VLDB
1998
ACM
112views Database» more  VLDB 1998»
14 years 21 days ago
Incremental Clustering for Mining in a Data Warehousing Environment
Data warehouses provide a great deal of opportunities for performing data mining tasks such as classification and clustering. Typically, updates are collected and applied to the d...
Martin Ester, Hans-Peter Kriegel, Jörg Sander...
CVPR
2008
IEEE
14 years 10 months ago
Generalised blurring mean-shift algorithms for nonparametric clustering
Gaussian blurring mean-shift (GBMS) is a nonparametric clustering algorithm, having a single bandwidth parameter that controls the number of clusters. The algorithm iteratively sh...
Miguel Á. Carreira-Perpiñán
ICDE
2008
IEEE
157views Database» more  ICDE 2008»
14 years 10 months ago
TALE: A Tool for Approximate Large Graph Matching
Abstract-- Large graph datasets are common in many emerging database applications, and most notably in large-scale scientific applications. To fully exploit the wealth of informati...
Yuanyuan Tian, Jignesh M. Patel
VLDB
1999
ACM
224views Database» more  VLDB 1999»
14 years 23 days ago
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional sp...
Alexander Hinneburg, Daniel A. Keim
ICPR
2008
IEEE
14 years 9 months ago
Feature selection focused within error clusters
We propose a feature selection method that constructs each new feature by analysis of tight error clusters. This is a greedy, time-efficient forward selection algorithm that itera...
Henry S. Baird, Sui-Yu Wang