ON MATRIX MODIFICATION OF CLARANS CLUSTERING METHOD IN LARGE VIDEO SURVEILLANCE DATABASES

Bogucharskiy S, Mashtalir V.
Abstract: 
Clustering algorithms for Very Large Data Bases (VLDB) are observed in application with image and video processing. Such a specific case requires initial data presentation as multidimen-sional vectors. That is why matrix modifications of traditional k-medoids, Partitioning Around Medoids, Clustering LARge Applications and CLARA based on RANdomized Search methods are proposed. Benefits and drawbacks of them all are examined.
References: 

1. Han J., Kamber M. Data Mining: Concepts and Techniques. – 2-nd ed. – San Francisco: Morgan Kaufmann, 2006. – 800 p. 2. Gan G., Ma C., Wu J. Data Clustering: Theory, Algorithms, and Applications. – Philadelphia: SIAM, 2007. – 466 p. 3. Abonyi J., Feil B. Cluster Analysis for Data Mining and System Identification. – Basel: Birkhäuser, 2007. – 303 p. 4. Olson D.L., Dursun D. Advanced Data Mining Techniques. – Berlin: Springer, 2008. – 180 p. 5. Xu R., Wunsch D.C. Clustering. – Hoboken: John Wiley&Sons, 2008. – 358 p. 6. Kohonen T. Self-Organizing Maps. – 1-st ed. – Berlin: Springer, 1995. – 501 p. 7. Ng R.T., Han J. Efficient and clustering methods for spatial data mining // 20-th Int. Conf. on Very Large Data Bases. – Santiago de Chile, 1994. P.144-155. 8. Kaufman L., Rousseeuw P.J. Finding Groups in Data: An Introduction to Cluster Analysis. – N.Y.: John Wiley&Sons, 1990. – 342 p.