An Efficient Vector Quantization Codebook generation using a Triangle Inequality
Hyun-Jin Lee
- 30 Sep 2012
- Vol. 13, Iss: 3, pp 309-315
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TL;DR: This paper presented the triangle inequality based algorithm to select the active data and Experimental results show that the algorithm is superior to other methods in terms of the VQ codebook generation time.
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Abstract: Active data are the input data which are changed its membership as Vector Quantization codebook generation algorithm is processed. In the process of VQ codebook generation algorithm performed, the actual active data out of the entire input data will be less presented as the process is performed. Therefore, if we can accurately find the active data and only if we are going to do VQ codebook generation on the active data, then we can significantly reduce the overall generation time. In this paper, we presented the triangle inequality based algorithm to select the active data. Experimental results show that our algorithm is superior to other methods in terms of the VQ codebook generation time.
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Citations
Online VQ Codebook Generation using a Triangle Inequality
Hyunjin Lee
- 30 Jun 2015
TL;DR: An online V Q Codebook generation method for updating an existing VQ Codebook in real-time and adding to an existing cluster with newly created text data which are news paper, web pages, blogs, tweets and IoT data like sensor, machine.
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Tian Zhang,Raghu Ramakrishnan,Miron Livny +2 more
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Data Clustering: Theory, Algorithms, and Applications
Guojun Gan,Chaoqun Ma,Jianhong Wu +2 more
- 12 Jul 2007
TL;DR: Clustering, Data and Similarity Measures: 1. data clustering 2. data types 3. scale conversion 4. data standardization and transformation 5. data visualization 6. Similarity and dissimilarity measures 7. clustering Algorithms.
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Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Guojun Gan,Chaoqun Ma,Jianhong Wu +2 more
- 01 May 2007
Abstract: Preface Part I. Clustering, Data and Similarity Measures: 1. Data clustering 2. DataTypes 3. Scale conversion 4. Data standardization and transformation 5. Data visualization 6. Similarity and dissimilarity measures Part II. Clustering Algorithms: 7. Hierarchical clustering techniques 8. Fuzzy clustering algorithms 9. Center Based Clustering Algorithms 10. Search based clustering algorithms 11. Graph based clustering algorithms 12. Grid based clustering algorithms 13. Density based clustering algorithms 14. Model based clustering algorithms 15. Subspace clustering 16. Miscellaneous algorithms 17. Evaluation of clustering algorithms Part III. Applications of Clustering: 18. Clustering gene expression data Part IV. Matlab and C++ for Clustering: 19. Data clustering in Matlab 20. Clustering in C/C++ A. Some clustering algorithms B. Thekd-tree data structure C. Matlab Codes D. C++ Codes Subject index Author index.
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