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» Measuring the Quality of Approximated Clusterings
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BIODATAMINING
2008
96views more  BIODATAMINING 2008»
13 years 5 months ago
Fast approximate hierarchical clustering using similarity heuristics
Background: Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that...
Meelis Kull, Jaak Vilo
KAIS
2006
110views more  KAIS 2006»
13 years 4 months ago
Multi-step density-based clustering
Abstract. Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex dista...
Stefan Brecheisen, Hans-Peter Kriegel, Martin Pfei...
CSIE
2009
IEEE
13 years 11 months ago
Evaluating Clustering Algorithms: Cluster Quality and Feature Selection in Content-Based Image Clustering
The paper presents an evaluation of four clustering algorithms: k-means, average linkage, complete linkage, and Ward’s method, with the latter three being different hierarchical...
Mesfin Sileshi, Björn Gambäck
JOI
2007
99views more  JOI 2007»
13 years 4 months ago
Measuring quality of similarity functions in approximate data matching
This paper presents a method for assessing the quality of similarity functions. The scenario taken into account is that of approximate data matching, in which it is necessary to d...
Roberto da Silva, Raquel Kolitski Stasiu, Viviane ...
ECIR
2006
Springer
13 years 6 months ago
Improving Quality of Search Results Clustering with Approximate Matrix Factorisations
Abstract. In this paper we show how approximate matrix factorisations can be used to organise document summaries returned by a search engine into meaningful thematic categories. We...
Stanislaw Osinski