Summary
Sequence analysis is a challenging task in the data mining arena, relevant for many practical domains. We propose a novel method for visual analysis and classification of sequences based on Iterated Function System (IFS). IFS is utilized to produce a fractal representation of sequences. The proposed method offers an effective tool for visual detection of sequence patterns influencing a target attribute, and requires no understanding of mathematical or statistical algorithms. Moreover, it enables to detect sequence patterns of any length, without predefining the sequence pattern length. It also enables to visually distinguish between different sequence patterns in cases of reoccurrence of categories within a sequence. Our proposed method provides another significant added value by enabling the visual detection of rare and missing sequences per target class.
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References
Arbel, R. and Rokach, L., Classifier evaluation under limited resources, Pattern Recognition Letters, 27(14): 1619–1631, 2006, Elsevier.
Barnsley M., Fractals Everywhere, Academic Press, Boston, 1988
Barnsley, M., Hurd L. P., Fractal Image Compression, A. K. Peters, Boston, 1993
Cohen S., Rokach L., Maimon O., Decision Tree Instance Space Decomposition with Grouped Gain-Ratio, Information Science, Volume 177, Issue 17, pp. 3592-3612, 2007.
Da Cunha C., Agard B., and Kusiak A., Data mining for improvement of product quality, International Journal of Production Research, 44(18-19), pp. 4027-4041, 2006
Falconer K., Techniques in Fractal geometry, John Wiley & Sons, 1997
Jeffrey H. J., Chaos game representation of genetic sequences, Nucleic Acids Res., vol. 18, pp. 2163 – 2170, 1990
Keim D. A., Information Visualization and Visual Data mining, IEEE Transactions of Visualization and Computer Graphics, Vol. 7, No. 1, pp. 100-107, 2002
Maimon O., and Rokach, L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study, in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha (ed.), Kluwer Academic Publishers, pp. 311–336, 2001.
Moskovitch R, Elovici Y, Rokach L, Detection of unknown computer worms based on behavioral classification of the host, Computational Statistics and Data Analysis, 52(9):4544–4566, 2008.
Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993
Rokach L., Mining manufacturing data using genetic algorithm-based feature set decomposition, Int. J. Intelligent Systems Technologies and Applications, 4(1):57-78, 2008.
Rokach L., Genetic algorithm-based feature set partitioning for classification problems, Pattern Recognition, 41(5):1676–1700, 2008.
Rokach, L., Decomposition methodology for classification tasks: a meta decomposer framework, Pattern Analysis and Applications, 9(2006):257–271.
Rokach, L. and Maimon, O., Theory and applications of attribute decomposition, IEEE International Conference on Data Mining, IEEE Computer Society Press, pp. 473–480, 2001.
Rokach L. and Maimon O., Feature Set Decomposition for Decision Trees, Journal of Intelligent Data Analysis, Volume 9, Number 2, 2005b, pp 131–158.
Rokach L., and Maimon O., Data mining for improving the quality of manufacturing: A feature set decomposition approach. Journal of Intelligent Manufacturing, 17(23.3), pp. 285-299, 2006
Rokach, L. and Maimon, O. and Arbel, R., Selective voting-getting more for less in sensor fusion, International Journal of Pattern Recognition and Artificial Intelligence 20 (3) (2006), pp. 329–350.
Rokach, L. and Maimon, O. and Averbuch, M., Information Retrieval System for Medical Narrative Reports, Lecture Notes in Artificial intelligence 3055, page 217-228 Springer-Verlag, 2004.
Rokach L., Maimon O. and Lavi I., Space Decomposition In Data Mining: A Clustering Approach, Proceedings of the 14th International Symposium On Methodologies For Intelligent Systems, Maebashi, Japan, Lecture Notes in Computer Science, Springer-Verlag, 2003, pp. 24–31.
Rokach L., Romano R. and Maimon O., Mining manufacturing databases to discover the effect of operation sequence on the product quality, Journal of Intelligent Manufacturing, 2008
Ruschin-Rimini N., Maimon O. and Romano R., Visual Analysis of Quality-related Manufacturing Data Using Fractal Geometry, working paper submitted for publication, 2009.
Weiss C. H., Visual Analysis of Categorical Time Series, Statistical Methodology 5, pp. 56- 71, 2008
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Rimini, N.R., Maimon, O. (2009). Visual Analysis of Sequences Using Fractal Geometry. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_29
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DOI: https://doi.org/10.1007/978-0-387-09823-4_29
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