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.
Pleiades provides 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.
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Keywords
- Subspace Cluster
- Open Interface
- Evaluation Interface
- Subspace Cluster Algorithm
- Practical Machine Learn Tool
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Assent, I., Müller, E., Krieger, R., Jansen, T., Seidl, T. (2008). Pleiades: Subspace Clustering and Evaluation. In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87481-2_44
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DOI: https://doi.org/10.1007/978-3-540-87481-2_44
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