OpenSubspace: An Open Source Framework for Evaluation and Exploration of Subspace Clustering Algorithms in WEKA

Subspace clustering and projected clustering are recent research areas for clustering in high dimensional spaces. As the field is rather young, there is a lack of comparative studies on the advantages and disadvantages of the different algorithms. Part of the underlying problem is the lack of available open source implementations that could be used by researchers to understand, compare, and extend subspace and projected clustering algorithms. In this paper, we discuss the requirements for open source evaluation software. We propose OpenSubspace, an open source framework that meets these requirements. OpenSubspace integrates state-of-the-art performance measures and visualization techniques to foster research in subspace and projected clustering.

Authors: Müller E., Assent I., Günnemann S., Jansen T., Seidl T.
Published in: Proc. 1st Open Source in Data Mining Workshop (OSDM 2009) in conjunction with 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand
Sprache: EN
Jahr: 2009
Seiten: 2-13
Konferenz: PAKDD
URL:OpenSubspace Project
OSDM 2009
Typ: Tagungsbeiträge
Forschungsgebiet: Data Analysis and Knowledge Extraction