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Data Mining and Knowledge Discovery Handbook, 2 Edition part 62

Data Mining and Knowledge Discovery Handbook, 2 Edition part 62. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 29 Visual Analysis of Sequences Using Fractal Geometry Noa Ruschin Rimini and Oded Maimon Department of Industrial Engineering Tel-Aviv University Tel-Aviv Israel 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. 29.1 Introduction Mining sequential data is an important challenge relevant for many practical domains such as analysis of the impact of operation sequence on product quality See Da Cunha et al. 2006 Rokach et al. 2008 and Ruschin-Rimini et al. 2009 analysis of customers purchase history for determining the next best offer analysis of products failure history for the purpose of root cause analysis security Moskovitch et al. 2008 and more. This chapter presents a novel approach for detecting sequence patterns that influence a target attribute and therefore act as sequence classifiers. It extends existing methods by providing a visual application enabling domain experts such as production engineers sales and customer service managers to visually detect sequence patterns that affect a target attribute. Moreover the proposed method overcomes limitations of existing methods such as the n-gram approach utilized by Da Cunha et al. 2006 by enabling the .