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» Approximate data mining in very large relational data
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NN
2006
Springer
113views Neural Networks» more  NN 2006»
15 years 1 months ago
Large-scale data exploration with the hierarchically growing hyperbolic SOM
We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H2 SOM) featuring two extensions of the HSOM (hyperbolic SOM): (i) a hierarchically growing variant that al...
Jörg Ontrup, Helge Ritter
KDD
2009
ACM
611views Data Mining» more  KDD 2009»
16 years 2 months ago
Fast approximate spectral clustering
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
Donghui Yan, Ling Huang, Michael I. Jordan
IDA
2009
Springer
14 years 11 months ago
Mining the Temporal Dimension of the Information Propagation
In the last decade, Social Network Analysis has been a field in which the effort devoted from several researchers in the Data Mining area has increased very fast. Among the possibl...
Michele Berlingerio, Michele Coscia, Fosca Giannot...
SDM
2007
SIAM
143views Data Mining» more  SDM 2007»
15 years 3 months ago
Less is More: Compact Matrix Decomposition for Large Sparse Graphs
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
Jimeng Sun, Yinglian Xie, Hui Zhang, Christos Falo...
VLDB
1998
ACM
105views Database» more  VLDB 1998»
15 years 5 months ago
Computing Iceberg Queries Efficiently
Many applications compute aggregate functions over an attribute (or set of attributes) to find aggregate values above some specified threshold. We call such queries iceberg querie...
Min Fang, Narayanan Shivakumar, Hector Garcia-Moli...