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: 2012
Pages: 17
Source title: Pattern Discovery Using Sequence Data Mining: Applications and Studies
Source Author(s)/Editor(s): Pradeep Kumar (Indian Institute of Management, India), P. Radha Krishna (Infosys Technologies Limited, India)and S. Bapi Raju (University of Hyderabad, India)
DOI: 10.4018/978-1-61350-056-9.ch006

Keywords: Data Mining / Data Mining and Databases / Engineering 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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