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BMCBI
2008
95views more  BMCBI 2008»
13 years 7 months ago
Methodology capture: discriminating between the "best" and the rest of community practice
Background: The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become...
James M. Eales, John W. Pinney, Robert D. Stevens,...
SIGMOD
1999
ACM
183views Database» more  SIGMOD 1999»
13 years 12 months ago
OPTICS: Ordering Points To Identify the Clustering Structure
Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysi...
Mihael Ankerst, Markus M. Breunig, Hans-Peter Krie...
AUSDM
2007
Springer
110views Data Mining» more  AUSDM 2007»
14 years 1 months ago
Useful Clustering Outcomes from Meaningful Time Series Clustering
Clustering time series data using the popular subsequence (STS) technique has been widely used in the data mining and wider communities. Recently the conclusion was made that it i...
Jason Chen
HM
2005
Springer
98views Optimization» more  HM 2005»
14 years 1 months ago
A Hybrid GRASP with Data Mining for the Maximum Diversity Problem
Abstract. The maximum diversity problem (MDP) consists in identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse ch...
L. F. Santos, Marcos Henrique Ribeiro, Alexandre P...
ISMIS
2005
Springer
14 years 1 months ago
Using Supervised Clustering to Enhance Classifiers
Abstract. This paper centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering is applied to classified examples ...
Christoph F. Eick, Nidal M. Zeidat