Abstract
Hierarchical clustering is very versatile in real world applications. However, due to the issue of higher computational complexity from which automated hierarchical clustering algorithms suffer, the user can hardly correct possible misclassifications from the tree-structured nature of clusters. Visualization is a powerful technique for data analysis, however, most of the existing cluster visualization techniques are mainly used for displaying clustering results. In order for the user to be directly involved in the process of discovering nested cluster structures, we introduce a visualization technique, called HOV3, to detect clusters and their internal cluster structure. As a result, our approach provides the user an effective method for the discovery of nested cluster structures by visualization.
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References
Andritsos, P., Tsaparas, P., Miller, R.J., Sevcik, K.C.: Limbo: scalable clustering of categorical data. In: Naves, G., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Sonnerat, N. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 123–146. Springer, Heidelberg (2004)
Eisen M.: Cluster and TreeView Manual (2007), http://rana.lbl.gov/manuals/ClusterTreeView.pdf
Fry, B.: Visualizing Data, Exploring and Explaining Data with the Processing Environment. O’Reilly Media, Sebastopol (2008)
Guha, S., Rastogh, R., Shim, K.: CURE: An efficient clustering algorithm for large databases. In: Proceedings of ACM SIGMOD Conference 1998, pp. 73–84 (1998)
GhasemiGol, M., Yazdi, H.S., Monsefi, R.: A New Hierarchical Clustering Algorithm on Fuzzy Data (FHCA). International Journal of Computer and Electrical Engineering 2(1), 1793–8163 (2010)
Kandogan, E.: Visualizing multi-dimensional clusters, trends, and outliers using star coordinates. In: Proc. of ACM SIGKDD Conference, pp. 107–116 (2001)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data, An Introduction to Cluster Analysis. John Wiley and Sons, Inc., Chichester (2005)
Karypis, G., Han, E.-H.S., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. IEEE Computer 32(8), 68–75 (1999)
http://www.bioinf.manchester.ac.uk/microarray/maxd/maxdView/overview.html
Sprenger, T.C., Brunella, R., Gross, M.H.: H-BLOB: A Hierarchical Visual Clustering Method Using Implicit Surfaces. In: Proc. of the conference on Visualization 2000, pp. 61–68. IEEE Computer Society Press, Los Alamitos (2000)
Swayne, D.F., Cook, D., Buja, A.: XGobi: Interactive dynamic data visualization in the X Window System. Journal of Computational and Graphical Statistics 7(1), 113–130 (1998)
Schonlau, M.: Visualizing non-hierarchical and hierarchical cluster analyses with clustergrams. Journal of Computational Statistics 19(1), 95–111 (2004)
Seo, J., Shneiderman, B.: Interactively Exploring Hierarchical Clustering Results. IEEE Computer 35(7), 80–86 (2002)
Shneiderman, B.: Inventing discovery tools: Combining information visualization with data mining. Information Visualization 1, 5–12 (2002)
Todd, C.S., Toth, T.M., Robert, B.-F.: GraphClus, a MATLAB program for cluster analysis using graph theory. Journal of Computers and Geosciences 35, 1205–1213 (2009)
Ward, M.O.: XmdvTool: Integrating Multiple Methods for Visualizing Multivariate Data. In: Proceedings of IEEE Conference on Visualization 1994, pp. 326–333 (1994)
Xu, R., Wunsch, D.C.: Clustering. John Wiley and Sons, Inc., Chichester (2009)
Yang, L.: Visual Exploration of Large Relational Data Sets through 3D Projections and Footprint Splatting. IEEE Transactions on Knowledge and Data Engineering 15(6), 1460–1471 (2003)
Zhang, K.-B., Huang, M.L., Orgun, M.A., Nguyen, Q.V.: A Visual Method for High-dimensional Data Cluster Exploration. In: Chan, J.H. (ed.) ICONIP 2009, Part II. LNCS, vol. 5864, pp. 699–709. Springer, Heidelberg (2009)
Zhang, K.-B., Orgun, M.A., Busch, P.A., Nayak, A.C.: A Top-down Approach for Hierarchical Cluster Exploration by Visualization. In: The Sixth International Conference on Advanced Data Mining and Applications (ADMA 2010), Xian, China, November 19-21 (2010) (accepted)
Zhang, K.-B., Orgun, M.A., Zhang, K.: HOV3, An Approach for Cluster Analysis. In: Li, X., Zaïane, O.R., Li, Z.-h. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 317–328. Springer, Heidelberg (2006)
Zhang, K.-B., Orgun, M.A., Zhang, K.: A Prediction-based Visual Approach for Cluster Exploration and Cluster Validation by HOV3. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 336–349. Springer, Heidelberg (2007)
Zhang, T., Ramakrishana, R., Livny, M.: An Efficient Data Clustering Method for Very Large Database. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 103–114 (1996)
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Zhang, KB., Orgun, M.A., Zhao, Y., Nayak, A.C. (2010). The Discovery of Hierarchical Cluster Structures Assisted by a Visualization Technique. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_85
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DOI: https://doi.org/10.1007/978-3-642-17537-4_85
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