Abstract
Due to the vast amount and pace of high-dimensional data production, dimensionality reduction emerges as an important requirement in many application areas. In this paper, we introduce X-SDR, a prototype designed specifically for the deployment and assessment of dimensionality reduction techniques. X-SDR is an integrated environment for dimensionality reduction and knowledge discovery that can be effectively used in the data mining process. In the current version, it supports communication with different database management systems and integrates a wealth of dimensionality reduction algorithms both distributed and centralized. Additionally, it interacts with Weka thus enabling the exploitation of the data mining algorithms therein. Finally, X-SDR provides an API that enables the integration and evaluation of any dimensionality reduction algorithm.
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Magdalinos, P., Kapernekas, A., Mpiratsis, A., Vazirgiannis, M. (2010). X-SDR: An Extensible Experimentation Suite for Dimensionality Reduction. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Lecture Notes in Computer Science(), vol 6323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15939-8_43
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DOI: https://doi.org/10.1007/978-3-642-15939-8_43
Publisher Name: Springer, Berlin, Heidelberg
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