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Mass Estimation

Posted on by Robin Ding Leave a comment Machine Learning
5.0

This site contains four packages of Mass and mass-based density estimation.

1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB.

2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system.

3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. DEMassBayes.7z has jar file to be used with WEKA and a readme file listing parameters used. The source files are included in DEMassBayes_Source.7z.

4. The four package is MassTER includes source and JAR file to be used with WEKA system..

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Website http://mass-estimation.sourceforge.net
Tags
License GNU General Public License version 2.0 (GPLv2)
Features
  • 1. Mass Estimation (one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation)
  • Requirements: MATLAB (>=7.4 R2007a), libsvm package
  • Maintainer: Guang-Tong Zhou <zhouguangtong@gmail.com>
  • References: K.M. Ting, G.-T. Zhou, F.T. Liu and S.C. Tan, Mass Estimation and Its Applications, In: Proceedings of the 16th ACM SIGKDD Conference on knowledge Discovery and Data Mining (SIGKDD'10), Washington, DC, 2010.
  • This package was developed by Mr. Guang-Tong Zhou (zhouguangtong@gmail.com). For any problem concerning the code, please feel free to contact Mr. Zhou.
  • 2 & 3. DEMass (DEMass-DBSCAN) and DEMass-Bayes
  • References: K. M. Ting and T. Washio and J. R. Wells and F. T. Liu. Density Estimation based on Mass, Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 11), 2011
  • This package requires WEKA 3.7 or higher. This package is free for academic usage. You can run it at your own risk.
  • 4. MassTER
  • References: Ting, K. M. & Wells, J. R. Multi-Dimensional Mass Estimation and Mass-based Clustering Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 10), IEEE Computer Society Press, 2010, 511-520