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» Novel Auxiliary Techniques in Clustering
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SDM
2007
SIAM
184views Data Mining» more  SDM 2007»
13 years 9 months ago
Mining Naturally Smooth Evolution of Clusters from Dynamic Data
Many clustering algorithms have been proposed to partition a set of static data points into groups. In this paper, we consider an evolutionary clustering problem where the input d...
Yi Wang, Shi-Xia Liu, Jianhua Feng, Lizhu Zhou
AIEDU
2010
13 years 5 months ago
Supporting Collaborative Learning and E-Discussions Using Artificial Intelligence Techniques
An emerging trend in classrooms is the use of networked visual argumentation tools that allow students to discuss, debate, and argue with one another in a synchronous fashion about...
Bruce M. McLaren, Oliver Scheuer, Jan Miksatko
BWCCA
2010
13 years 2 months ago
Lifetime Security Improvement in Wireless Sensor Network Using Queue-Based Techniques
A wireless sensor network (WSN) is envisioned as a cluster of tiny power-constrained devices with functions of sensing and communications. Sensors closer to a sink node have a larg...
Fuu-Cheng Jiang, Hsiang-Wei Wu, Der-Chen Huang, Ch...
ICCV
2009
IEEE
1556views Computer Vision» more  ICCV 2009»
15 years 19 days ago
Kernel Methods for Weakly Supervised Mean Shift Clustering
Mean shift clustering is a powerful unsupervised data analysis technique which does not require prior knowledge of the number of clusters, and does not constrain the shape of th...
Oncel Tuzel, Fatih Porikli, Peter Meer
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
14 years 8 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis