Privacy-preserving clustering with distributed EM mixture modeling
β Scribed by Xiaodong Lin; Chris Clifton; Michael Zhu
- Book ID
- 106280214
- Publisher
- Springer-Verlag
- Year
- 2005
- Tongue
- English
- Weight
- 276 KB
- Volume
- 8
- Category
- Article
- ISSN
- 0219-1377
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the
This paper presents new robust clustering algorithms, which signiΓΏcantly improve upon the noise and initialization sensitivity of traditional mixture decomposition algorithms, and simplify the determination of the optimal number of clusters in the data set. The algorithms implement maximum likelihoo
Basing cluster analysis on mixture models has become a classical and powerful approach. Until now, this approach, which allows to explain some classic clustering criteria such as the well-known k-means criteria and to propose general criteria, has been developed to classify a set of objects measured