Abstract
Original language | English |
---|---|
Pages (from-to) | 637-649 |
Number of pages | 13 |
Journal | Pattern Recognition |
Volume | 38 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 May 2005 |
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Keywords
- Cluster analysis
- Clustering feature
- Convergence
- Data mining
- Expectation maximization
- Gaussian mixture model
- Scalable
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Scalable model-based cluster analysis using clustering features. / JIN, Huidong; LEUNG, Kwong Sak; WONG, Man Leung; XU, Zong Ben.
In: Pattern Recognition, Vol. 38, No. 5, 01.05.2005, p. 637-649.Research output: Journal Publications › Journal Article (refereed)
TY - JOUR
T1 - Scalable model-based cluster analysis using clustering features
AU - JIN, Huidong
AU - LEUNG, Kwong Sak
AU - WONG, Man Leung
AU - XU, Zong Ben
PY - 2005/5/1
Y1 - 2005/5/1
N2 - We present two scalable model-based clustering systems based on a Gaussian mixture model with independent attributes within clusters. They first summarize data into sub-clusters, and then generate Gaussian mixtures from their clustering features using a new algorithm - EMACF. EMACF approximates the aggregate behavior of each sub-cluster of data items in the Gaussian mixture model. It provably converges. The experiments show that our clustering systems run one or two orders of magnitude faster than the traditional EM algorithm with few losses of accuracy.
AB - We present two scalable model-based clustering systems based on a Gaussian mixture model with independent attributes within clusters. They first summarize data into sub-clusters, and then generate Gaussian mixtures from their clustering features using a new algorithm - EMACF. EMACF approximates the aggregate behavior of each sub-cluster of data items in the Gaussian mixture model. It provably converges. The experiments show that our clustering systems run one or two orders of magnitude faster than the traditional EM algorithm with few losses of accuracy.
KW - Cluster analysis
KW - Clustering feature
KW - Convergence
KW - Data mining
KW - Expectation maximization
KW - Gaussian mixture model
KW - Scalable
UR - http://commons.ln.edu.hk/sw_master/2385
U2 - 10.1016/j.patcog.2004.07.012
DO - 10.1016/j.patcog.2004.07.012
M3 - Journal Article (refereed)
VL - 38
SP - 637
EP - 649
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
IS - 5
ER -