Abstract
In this paper we present a visual approach for classification in data mining, based on the enhanced separation feature of a visual technique, called Hypothesis-Oriented Verification and Validation by Visualization (HOV3). In this approach, the user first projects a labeled dataset by HOV3with a statistical measurement of the dataset on a 2d space, where data points with the same class label are well separated into groups. Then each well separated group and its measure vector are employed as a visual classifier to classify unlabeled data points by projecting and grouping them together with the overlapping labeled data points. The experiments demonstrate that our approach is effective to assist the user on classification of data by visualization.
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References
Ankerst, M., Ester, M., Kriegel, H.-P.: Towards an effective cooperation of the user and the computer for classification. In: Proceedings of KDD 2000, pp. 179–188. ACM press (2000)
Bellman, R.E.: Dynamic programming. Princeton University Press (1957)
Inselberg, A.: Parallel Coordinates: Visualization, Exploration and Classification of High-Dimensional Data. In: Inselberg, A. (ed.) Handbook of Data Visualization, Springer Handbooks of Computational Statistics, vol. III, pp. 643–680 (2008)
Rehm, F., Klawonn, F., Kruse, R.: Rule Classification Visualization of High-Dimensional Data. In: Proceedings of IPMU 2006, pp. 1944–1948 (2006)
Teoh, S.T., Ma, K.-L.: StarClass: Interactive Visual Classification Using Star Coordinates. In: Proceedings of SDM 2003, pp. 178–185 (2003)
Witten, L.H., Frank, E., Hall, M.A.: Data Mining Practical Machine Learning Tools and Technologies. Morgan Kaufmann Publishers (2011)
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, K.-B., Orgun, M.A., Zhang, K.: Enhanced Visual Separation of Clusters by M-Mapping to Facilitate Cluster Analysis. In: Qiu, G., Leung, C., Xue, X.-Y., Laurini, R. (eds.) VISUAL 2007. LNCS, vol. 4781, pp. 285–297. Springer, Heidelberg (2007)
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Zhang, KB., Orgun, M.A., Shankaran, R., Zhang, D. (2012). A Visual Approach for Classification Based on Data Projection. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_84
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DOI: https://doi.org/10.1007/978-3-642-32695-0_84
Publisher Name: Springer, Berlin, Heidelberg
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