Efficient clustering algorithm for large datasets

Clustering, in data mining, is useful for identifying interesting distributions and discovering groups in the underlying data. Traditional clustering algorithms either favor clusters with similar sizes and spherical shapes, or are very sensitive to outliers. These shortcomings are alleviated in a no...

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Main Author: Chen, Fangying.
Other Authors: Chen Lihui
Format: Final Year Project
Language:English
Published: 2010
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Online Access:http://hdl.handle.net/10356/40791
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-407912023-07-07T17:09:15Z Efficient clustering algorithm for large datasets Chen, Fangying. Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Clustering, in data mining, is useful for identifying interesting distributions and discovering groups in the underlying data. Traditional clustering algorithms either favor clusters with similar sizes and spherical shapes, or are very sensitive to outliers. These shortcomings are alleviated in a novel algorithm called CURE which was proposed by some researchers. CURE achieves the improvement by representing each cluster with a constant number of well-scattered points from the cluster and then shrinking them toward the center of the cluster by a specified fraction. In an effort to keep up with the rapid growth in the size of databases, CURE incorporates two techniques, random sampling and partitioning, to cope with large datasets. The tenet of both techniques is to reduce the input size to clustering process in order to fit in the main memory. Nowadays, high dimensional data is commonly found in a wide range of real-life applications, like web documents, transaction data and gene expression data. There is an urge for efficient high dimensional data clustering. In this Final Year Project, CURE algorithm is first implemented for low dimensional data with Java programming language. The program is tested on sample datasets. A series of simulations with different parameter settings are carried out and a parameter sensitivity analysis is performed. After being verified on low dimensional data, the program is modified to deal with high dimensional data. Later, the modified program is tested on high dimensional sample datasets and a parameter analysis is performed as well. The objective of this project is to implement CURE using Java. The implementation details, the testing results and performance evaluation are reported. Bachelor of Engineering 2010-06-22T02:10:52Z 2010-06-22T02:10:52Z 2010 2010 Final Year Project (FYP) http://hdl.handle.net/10356/40791 en Nanyang Technological University 69 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::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Chen, Fangying.
Efficient clustering algorithm for large datasets
description Clustering, in data mining, is useful for identifying interesting distributions and discovering groups in the underlying data. Traditional clustering algorithms either favor clusters with similar sizes and spherical shapes, or are very sensitive to outliers. These shortcomings are alleviated in a novel algorithm called CURE which was proposed by some researchers. CURE achieves the improvement by representing each cluster with a constant number of well-scattered points from the cluster and then shrinking them toward the center of the cluster by a specified fraction. In an effort to keep up with the rapid growth in the size of databases, CURE incorporates two techniques, random sampling and partitioning, to cope with large datasets. The tenet of both techniques is to reduce the input size to clustering process in order to fit in the main memory. Nowadays, high dimensional data is commonly found in a wide range of real-life applications, like web documents, transaction data and gene expression data. There is an urge for efficient high dimensional data clustering. In this Final Year Project, CURE algorithm is first implemented for low dimensional data with Java programming language. The program is tested on sample datasets. A series of simulations with different parameter settings are carried out and a parameter sensitivity analysis is performed. After being verified on low dimensional data, the program is modified to deal with high dimensional data. Later, the modified program is tested on high dimensional sample datasets and a parameter analysis is performed as well. The objective of this project is to implement CURE using Java. The implementation details, the testing results and performance evaluation are reported.
author2 Chen Lihui
author_facet Chen Lihui
Chen, Fangying.
format Final Year Project
author Chen, Fangying.
author_sort Chen, Fangying.
title Efficient clustering algorithm for large datasets
title_short Efficient clustering algorithm for large datasets
title_full Efficient clustering algorithm for large datasets
title_fullStr Efficient clustering algorithm for large datasets
title_full_unstemmed Efficient clustering algorithm for large datasets
title_sort efficient clustering algorithm for large datasets
publishDate 2010
url http://hdl.handle.net/10356/40791
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