Support vector machines based on K-means clustering for real-time business intelligence systems

  title={Support vector machines based on K-means clustering for real-time business intelligence systems},
  author={Jiaqi Wang and Xindong Wu and Chengqi Zhang},
Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response of SVM classifiers by reducing the number of support vectors. This is done by the K-means SVM (KMSVM… CONTINUE READING
Highly Cited
This paper has 111 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 52 extracted citations

A local mixture based SVM for an efficient supervised binary classification

The 2013 International Joint Conference on Neural Networks (IJCNN) • 2013
View 6 Excerpts
Highly Influenced

An ordinal kernel trick for a computationally efficient support vector machine

2014 International Joint Conference on Neural Networks (IJCNN) • 2014
View 4 Excerpts
Highly Influenced

Support Vector Machine Using k-Spatial Medians Clustering and Recovery Process

Communications in Statistics - Simulation and Computation • 2010
View 4 Excerpts
Highly Influenced

Automatic identification of acacia leaf diseases in plantation forests using wavelet energy and Shannon entropy

2017 International Conference on Information and Communication Technology Convergence (ICTC) • 2017

Compact kernel classifiers trained with minimum classification error criterion

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) • 2017
View 1 Excerpt

Dissolved Gas Analysis of power transformer using K-means and Support Vector Machine

2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) • 2016

111 Citations

Citations per Year
Semantic Scholar estimates that this publication has 111 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 14 references

PAC bounds for hold-out procedures’, Cross-Validation, Bootstrap and Model Selection Workshop, Advances in Neural Information Processing Systems

J. Langford
View 4 Excerpts
Highly Influenced

Statistical learning theory

View 5 Excerpts
Highly Influenced

SVM-OD: a new SVM algorithm for outlier detection

J. Q. Wang, C. Q. Zhang, X. D. Wu, H. W. Qi, J. Wang
Foundations and New Directions of Data Mining Workshop in IEEE International Conference of Data Mining • 2003

Kernel projection algorithm for large-scale SVM problems

Journal of Computer Science and Technology • 2002
View 1 Excerpt

Support vector machine classification and validation of cancer tissue samples using microarray expression data

T. S. Furey, N. Cristianini, D. W. Bednarski, M. Schummer, D. Haussler
Bioinformatics, Vol. 16, • 2000
View 2 Excerpts

Similar Papers

Loading similar papers…