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DAWAK
2010
Springer
15 years 5 months ago
Frequent Sub-graph Mining on Edge Weighted Graphs
Frequent sub-graph mining entails two significant overheads. The first is concerned with candidate set generation. The second with isomorphism checking. These are also issues with ...
Chuntao Jiang, Frans Coenen, Michele Zito
DATAMINE
1999
140views more  DATAMINE 1999»
15 years 4 months ago
A Scalable Parallel Algorithm for Self-Organizing Maps with Applications to Sparse Data Mining Problems
Abstract. We describe a scalable parallel implementation of the self organizing map (SOM) suitable for datamining applications involving clustering or segmentation against large da...
Richard D. Lawrence, George S. Almasi, Holly E. Ru...
COMPGEOM
2011
ACM
14 years 8 months ago
Metric graph reconstruction from noisy data
Many real-world data sets can be viewed of as noisy samples of special types of metric spaces called metric graphs [16]. Building on the notions of correspondence and GromovHausdo...
Mridul Aanjaneya, Frédéric Chazal, D...
RECOMB
2009
Springer
16 years 5 months ago
Spatial Clustering of Multivariate Genomic and Epigenomic Information
The combination of fully sequence genomes and new technologies for high density arrays and ultra-rapid sequencing enables the mapping of generegulatory and epigenetics marks on a g...
Rami Jaschek, Amos Tanay
KDD
2004
ACM
211views Data Mining» more  KDD 2004»
16 years 5 months ago
Towards parameter-free data mining
Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. First, incorrect settings ...
Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) R...