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ICANN
2007
Springer
16 years 28 days ago
Split-Merge Incremental LEarning (SMILE) of Mixture Models
In this article we present an incremental method for building a mixture model. Given the desired number of clusters K ≥ 2, we start with a two-component mixture and we optimize t...
Konstantinos Blekas, Isaac E. Lagaris
ACL
1997
15 years 8 months ago
Document Classification Using a Finite Mixture Model
We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of cla...
Hang Li, Kenji Yamanishi
CVPR
2008
IEEE
16 years 8 months ago
Generalised blurring mean-shift algorithms for nonparametric clustering
Gaussian blurring mean-shift (GBMS) is a nonparametric clustering algorithm, having a single bandwidth parameter that controls the number of clusters. The algorithm iteratively sh...
Miguel Á. Carreira-Perpiñán
KDD
2009
ACM
206views Data Mining» more  KDD 2009»
16 years 7 months ago
Ranking-based clustering of heterogeneous information networks with star network schema
A heterogeneous information network is an information network composed of multiple types of objects. Clustering on such a network may lead to better understanding of both hidden s...
Yizhou Sun, Yintao Yu, Jiawei Han
CCGRID
2001
IEEE
15 years 10 months ago
Cluster Computing in the Classroom: Topics, Guidelines, and Experiences
- With the progress of research on cluster computing, more and more universities have begun to offer various courses covering cluster computing. A wide variety of content can be ta...
Amy W. Apon, Rajkumar Buyya, Hai Jin, Jens Mache