High-dimensional multi-view dataset pre-processing and clustering
The objective of this project is two-fold: the first one is to perform pre-processing on high-dimensional multi-view datasets and to investigate potential applications of new data mining techniques with the new datasets. The second part of the project is to design and implement an existing...
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sg-ntu-dr.10356-498812023-07-07T16:03:50Z High-dimensional multi-view dataset pre-processing and clustering Wei, Yaguang. Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems The objective of this project is two-fold: the first one is to perform pre-processing on high-dimensional multi-view datasets and to investigate potential applications of new data mining techniques with the new datasets. The second part of the project is to design and implement an existing clustering algorithm and conduct extensive experimental study to test the performances of the clustering algorithm on various text benchmark datasets. This report highlights the implementation of the pre-processing approach for the data processing. It also includes the design and implementation ideas of the clustering algorithm Semi-Supervised Spherical K-Means and the simulation results. The performances of SS-SKM are documented and reported. Bachelor of Engineering 2012-05-25T04:02:04Z 2012-05-25T04:02:04Z 2012 2012 Final Year Project (FYP) http://hdl.handle.net/10356/49881 en Nanyang Technological University 40 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Wei, Yaguang. High-dimensional multi-view dataset pre-processing and clustering |
description |
The objective of this project is two-fold: the first one is to perform pre-processing on
high-dimensional multi-view datasets and to investigate potential applications of new
data mining techniques with the new datasets.
The second part of the project is to design and implement an existing clustering
algorithm and conduct extensive experimental study to test the performances of the
clustering algorithm on various text benchmark datasets. This report highlights the
implementation of the pre-processing approach for the data processing. It also includes
the design and implementation ideas of the clustering algorithm Semi-Supervised
Spherical K-Means and the simulation results. The performances of SS-SKM are
documented and reported. |
author2 |
Chen Lihui |
author_facet |
Chen Lihui Wei, Yaguang. |
format |
Final Year Project |
author |
Wei, Yaguang. |
author_sort |
Wei, Yaguang. |
title |
High-dimensional multi-view dataset pre-processing and clustering |
title_short |
High-dimensional multi-view dataset pre-processing and clustering |
title_full |
High-dimensional multi-view dataset pre-processing and clustering |
title_fullStr |
High-dimensional multi-view dataset pre-processing and clustering |
title_full_unstemmed |
High-dimensional multi-view dataset pre-processing and clustering |
title_sort |
high-dimensional multi-view dataset pre-processing and clustering |
publishDate |
2012 |
url |
http://hdl.handle.net/10356/49881 |
_version_ |
1772825966598422528 |