Pleiades: Subspace Clustering and Evaluation

Authors:
Ira Assent;Emmanuel Müller;Ralph Krieger;Timm Jansen;Thomas Seidl
Affiliations:
Data management and exploration group, RWTH Aachen University, Germany;Data management and exploration group, RWTH Aachen University, Germany;Data management and exploration group, RWTH Aachen University, Germany;Data management and exploration group, RWTH Aachen University, Germany;Data management and exploration group, RWTH Aachen University, Germany
Venue:
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Year:
2008

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Abstract

Subspace clustering mines the clusters present in locally relevant subsets of the attributes. In the literature, several approaches have been suggested along with different measures for quality assessment.Pleiadesprovides the means for easy comparison and evaluation of different subspace clustering approaches, along with several quality measures specific for subspace clustering as well as extensibility to further application areas and algorithms. It extends the popular WEKA mining tools, allowing for contrasting results with existing algorithms and data sets.