Robust models and novel similarity measures for high-dimensional data clustering
The purpose of this thesis is to present our research works on some of the fundamental issues encountered in high-dimensional data clustering. From our study of the current literature, we list out a few important problems that are still open for solutions in the field, and propose the appropriate so...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Theses and Dissertations |
Language: | English |
Published: |
2012
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/48657 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-48657 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-486572023-07-04T16:13:19Z Robust models and novel similarity measures for high-dimensional data clustering Nguyen, Duc Thang Chan Chee Keong Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications The purpose of this thesis is to present our research works on some of the fundamental issues encountered in high-dimensional data clustering. From our study of the current literature, we list out a few important problems that are still open for solutions in the field, and propose the appropriate solutions for these problems. We investigate how statistics, machine learning and meta-heuristics techniques can be used to improve existing methods or develop novel models for unsupervised learning of high-dimensional data. Our goals are to develop efficient clustering algorithms that could reflect the natural properties of high-dimensional data, be robust to outliers and less sensitive to initialization; algorithm that are simple and fast, easily applicable and still produce good clustering quality. The main contributions of this thesis include a robust model-based clustering algorithm which is capable of handling noisy data, a novel similarity measure and its resulted algorithms for clustering text document data, and other related studies to help improve existing clustering algorithms. DOCTOR OF PHILOSOPHY (EEE) 2012-05-04T08:55:10Z 2012-05-04T08:55:10Z 2012 2012 Thesis Nguyen, D. T. (2012). Robust models and novel similarity measures for high-dimensional data clustering. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/48657 10.32657/10356/48657 en 169 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications |
spellingShingle |
DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications Nguyen, Duc Thang Robust models and novel similarity measures for high-dimensional data clustering |
description |
The purpose of this thesis is to present our research works on some of the fundamental issues encountered in high-dimensional data clustering. From our study of the current literature, we list out a few important problems that are still open for solutions in the field, and propose the appropriate solutions for these problems. We investigate how statistics, machine learning and meta-heuristics techniques can be used to improve existing methods or develop novel models for unsupervised learning of high-dimensional data. Our goals are to develop efficient clustering algorithms that could reflect the natural properties of high-dimensional data, be robust to outliers and less sensitive to initialization; algorithm that are simple and fast, easily applicable and still produce good clustering quality. The main contributions of this thesis include a robust model-based clustering algorithm which is capable of handling noisy data, a novel similarity measure and its resulted algorithms for clustering text document data, and other related studies to help improve existing clustering algorithms. |
author2 |
Chan Chee Keong |
author_facet |
Chan Chee Keong Nguyen, Duc Thang |
format |
Theses and Dissertations |
author |
Nguyen, Duc Thang |
author_sort |
Nguyen, Duc Thang |
title |
Robust models and novel similarity measures for high-dimensional data clustering |
title_short |
Robust models and novel similarity measures for high-dimensional data clustering |
title_full |
Robust models and novel similarity measures for high-dimensional data clustering |
title_fullStr |
Robust models and novel similarity measures for high-dimensional data clustering |
title_full_unstemmed |
Robust models and novel similarity measures for high-dimensional data clustering |
title_sort |
robust models and novel similarity measures for high-dimensional data clustering |
publishDate |
2012 |
url |
https://hdl.handle.net/10356/48657 |
_version_ |
1772827355189870592 |