Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications

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Author(s): T. Ravindra Babu (Infosys Limited, India), M. Narasimha Murty (Indian Institute of Science Bangalore, India)and S. V. Subrahmanya (Infosys Limited, India)
Copyright: 2013
Pages: 17
Source title: Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch038

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

Data Mining deals with efficient algorithms for dealing with large data. When such algorithms are combined with data compaction, they would lead to superior performance. Approaches to deal with large data include working with representatives of data instead of entire data. The representatives should preferably be generated with minimal data scans. In the current chapter we discuss working with methods of lossy and non-lossy data compression methods combined with clustering and classification of large datasets. We demonstrate the working of such schemes on two large data sets.

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