Cosine-Based Clustering Algorithm Approach

Full Text (PDF, 1135KB), PP.53-63

Views: 0 Downloads: 0


Mohammed A. H. Lubbad 1,* Wesam M. Ashour 1

1. Computer Engineering Dept. Faculty of engineering, Islamic university of gaza (IUG), Gaza, Palestine

* Corresponding author.


Received: 20 Mar. 2011 / Revised: 12 Jul. 2011 / Accepted: 3 Oct. 2011 / Published: 8 Feb. 2012

Index Terms

Discrete Cosine Transformation (DCT), Cosine Cluster, Wave Cluster, Wavelet Transformation, Spatial Data, multi-resolution and clusters


Due to many applications need the management of spatial data; clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shapes. It must be insensitive to the outliers (noise) and the order of input data. In this paper Cosine Cluster is proposed based on cosine transformation, which satisfies all the above requirements. Using multi-resolution property of cosine transforms, arbitrary shape clusters can be effectively identified at different degrees of accuracy. Cosine Cluster is also approved to be highly efficient in terms of time complexity. Experimental results on very large data sets are presented, which show the efficiency and effectiveness of the proposed approach compared to other recent clustering methods.

Cite This Paper

Mohammed A. H. Lubbad, Wesam M. Ashour, "Cosine-Based Clustering Algorithm Approach", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.1, pp.53-63, 2012. DOI:10.5815/ijisa.2012.01.07


[1]D. Allard and C. Fraley. Non parametric maximum likelihood estimation of features in spatial process using voronoi tesselation. Journal of the American Statistical Association, December 1997.Eli Fogel, Motti Gavish. Nth-order Dynamics Target Observability from Angle Measurements[J] . IEEE Trans. on AES, 1998, 3(24):305 - 307.

[2]S. Byers and A. E. Raftery. Nearest neighbor clutter removal for estimating features in spatial point processes. Technical Report 295, Department of Statistics, University of Washington, 1995.

[3]M. Ester, H. Kriegel, J. Sander, and X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of 2nd International Conference on KDD, 1996.

[4]E.J. Pauwels, P. Fiddelaers, and L. Van Gool. DOG-based unsupervized clustering for CBIR. In Proceedings of the 2nd International Conference on Visual Information Systems, pages 13-20, 1997.

[5]J. R. Smith and S. Chang. Transform Features For Texture Classification and Discrimination in Large Image Databases. In Proceedings of the IEEE International Conference on Image Processing, pages 407- 411,1994.

[6]Robert Schalkoff. Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons, Inc., 1992.

[7]Gilbert Strang and Truong Nguyen. Wavelets and Filter Banks. Wellesley-Cambridge Press, 1996.

[8]Y. Shiloach and U. Vishkin. An O(logn) parallel connectivity algorithm. Journal of Algorithms, 3:57-67, 1982.

[9]G. Sheikholeslami and A. Zhang. An Approach to Clustering Large Visual Databases Using Wavelet Transform. In Proceedings of the SPIE Conference on Visual Data Exploration and Analysis IV, San Jose, February 1997.

[10]G. Sheikholeslami, A. Zhang, and L. Bian. Geographical Data Classification and Retrieval. In Proceedings of the 5th ACM International Workshop on Geographic Information Systems, pages 58-61, 1997.

[11]Michael L. Hilton, Bjorn D. Jawerth, and Ayan Sengupta. Compressing Still and Moving Images with Wavelets. Multimedia Systems, 2(5):218-227, December 1994.

[12]Berthold Klaus Paul Horn. Robot Vision. The MIT Press, forth edition, 1988.

[13]L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990.

[14]S. Mallat. Multiresolution approximation and wavelet orthonormal bases of L2(R). Transactions of American Mathematical Society, 315:69-87, September 1989.

[15]S. Mallat. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trnasactions on Pattern Analysis and Machine Intelligence, 11:674693, July 1989.

[16]R. T. Ng and J. Han. Efficient and Effective Clustering Methods for Spatial Data Mining. In Proceedings of the 2Uth VLDB Conference, pages 144-155, 1994.

[17]D. Nassimi and S. Sahni. Finding connected components and connected ones on a mesh-connected parallel computer. SIAM Journal on Computing, 9:744-757, 1980.

[18]Greet Uytterhoeven, Dirk Roose, and Adhemar Bultheel. Wavelet transforms using lifting scheme. Technical Report ITA- Wavelets Report WP 1.1, Katholieke Universiteit Leuven, Department of Computer Science, Belgium, April 1997.

[19]P. P. Vaidyanathan. M&irate Systems And Filter Banks. Prentice Hall Signal Processing Series. Prentice Hall, Englewood Cliffs, NJ,, 1993.

[20]Wei Wang, Jiong Yang, and Richard Muntz. STING: A statistical information grid approach to spatial data mining. In Proceedings of the 23rd VLDB Conference, pages 186-195, Athens, Greece, 1997.

[21]Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the 1996 A CM SIGMOD International Conference on Management of Data, pages 103-114, Montreal, Canada, 1996.

[22]Sheikholeslami, Chatterjee, and Zhang WaveCluster : A Multi-Resolution Clustering Approach for Very Large Spatial Databases ACM (VLDB’98)

[23]Safdar Ali Syed Abedi, " Exploring Discrete Cosine Transform For Multi-Resolution Analysis " Under the Direction of Saeid Belkasim.