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dc.contributor.authorSarthak Agrawal.
dc.description.abstractIn this project, I study and investigate how one compares arbitrary length multivariate data sequences by projecting the data sequences into a fixed low-dimensional space. To enable the comparison, a similarity value between two data sequences are computed using the Longest Common Subsequence (LCSS) algorithm for all possible pairs of data sequences, followed by the projection of the data sequences into a low-dimensional space using the ISOMAP algorithm. The contributions of my project is (i) an approach to choose the LCSS parameters to enable a good dimensionality reduction (i.e. similar data sequences are closed to one another, and vice versa), and (ii) application of the comparison approach to the 29 North Indian Tropical cyclones occurring from 2007 to 2011.en_US
dc.format.extent65 p.en_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleArbitrary length multivariate sequence similarity : a case study on north indian tropical cyclonesen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisor2Ho Shen-Shyangen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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