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This dissertation by Prithiviraj Muthumanickam explores data abstraction and pattern identification in time-series data, focusing on large 1D and 2D data sets. It discusses methods to transform raw data into symbol sequences, enabling pattern identification through data mining and machine learning techniques. The research aims to simplify complex visual data analysis processes while retaining significant features, particularly in financial and eye-tracking data. The thesis demonstrates how these abstraction techniques facilitate interactive data exploration and visualization, aiding in the identification of temporal patterns. The intended audience includes researchers and professionals in data analysis and visualization fields.

Time-series analysis. --- Data mining. --- Time-series analysis --- Data mining

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Time-series analysis --- Time-series analysis --- R (Computer program language)

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Discourse analysis --- Time-series analysis.

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Climatology --- Time-series analysis --- Computer programs

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Functional analysis --- Mathematics --- analyse (wiskunde) --- time series analysis --- wiskunde