An improved self organizing map using jaccard new measure for textual bugs data clustering

In software projects there is a data repository which contains the bug reports. These bugs are required to carefully analyze to resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the delaying in addressing some important bugs resolutions. To overc...

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Main Author:	Ahmed, Attika
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA Mathematics QA1-43 General
Online Access:	http://eprints.uthm.edu.my/7554/1/24p%20ATTIKA%20AHMED.pdf http://eprints.uthm.edu.my/7554/2/ATTIKA%20AHMED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7554/3/ATTIKA%20AHMED%20WATERMARK.pdf
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spelling	my-uthm-ep.75542022-08-21T01:45:13Z An improved self organizing map using jaccard new measure for textual bugs data clustering 2018-01 Ahmed, Attika QA Mathematics QA1-43 General In software projects there is a data repository which contains the bug reports. These bugs are required to carefully analyze to resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the delaying in addressing some important bugs resolutions. To overcome this problem researchers have been introduced many techniques. One of the techniques is the bug clustering. For the purpose of clustering, a variety of clustering algorithms available. One of the commonly used algorithm for bug clustering is K-means, which is considered a simplest unsupervised learning algorithm for clustering, yet it tends to produce smaller number of cluster. Considering the unsupervised learning algorithms, SelfOrganizing Map (SOM) considers the equally compatible algorithm for clustering, as both algorithms are closely related but different in way they were used in data mining. This research attempts a comparative analysis of both the clustering algorithms and for attaining the results, a series of experiment has been conducted using Mozilla bugs data set. To address the data sparseness issue, the experiment has been perfonned on textual bugs' data by using two different distance measure which are Euclidean distance and Jaccard New Measure. The research results suggested that SOM has a limitation of poor perfonnance on sparse data set. Thus, the research introduced the improved SOM algorithm by using a Jaccard NM (SOM-JNM). The SOM-JNM produced significantly better results therefore; it can be consider a challenging approach to address the sparse data problems. 2018-01 Thesis http://eprints.uthm.edu.my/7554/ http://eprints.uthm.edu.my/7554/1/24p%20ATTIKA%20AHMED.pdf text en public http://eprints.uthm.edu.my/7554/2/ATTIKA%20AHMED%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/7554/3/ATTIKA%20AHMED%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA Mathematics QA1-43 General
spellingShingle	QA Mathematics QA1-43 General Ahmed, Attika An improved self organizing map using jaccard new measure for textual bugs data clustering
description	In software projects there is a data repository which contains the bug reports. These bugs are required to carefully analyze to resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the delaying in addressing some important bugs resolutions. To overcome this problem researchers have been introduced many techniques. One of the techniques is the bug clustering. For the purpose of clustering, a variety of clustering algorithms available. One of the commonly used algorithm for bug clustering is K-means, which is considered a simplest unsupervised learning algorithm for clustering, yet it tends to produce smaller number of cluster. Considering the unsupervised learning algorithms, SelfOrganizing Map (SOM) considers the equally compatible algorithm for clustering, as both algorithms are closely related but different in way they were used in data mining. This research attempts a comparative analysis of both the clustering algorithms and for attaining the results, a series of experiment has been conducted using Mozilla bugs data set. To address the data sparseness issue, the experiment has been perfonned on textual bugs' data by using two different distance measure which are Euclidean distance and Jaccard New Measure. The research results suggested that SOM has a limitation of poor perfonnance on sparse data set. Thus, the research introduced the improved SOM algorithm by using a Jaccard NM (SOM-JNM). The SOM-JNM produced significantly better results therefore; it can be consider a challenging approach to address the sparse data problems.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Ahmed, Attika
author_facet	Ahmed, Attika
author_sort	Ahmed, Attika
title	An improved self organizing map using jaccard new measure for textual bugs data clustering
title_short	An improved self organizing map using jaccard new measure for textual bugs data clustering
title_full	An improved self organizing map using jaccard new measure for textual bugs data clustering
title_fullStr	An improved self organizing map using jaccard new measure for textual bugs data clustering
title_full_unstemmed	An improved self organizing map using jaccard new measure for textual bugs data clustering
title_sort	improved self organizing map using jaccard new measure for textual bugs data clustering
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2018
url	http://eprints.uthm.edu.my/7554/1/24p%20ATTIKA%20AHMED.pdf http://eprints.uthm.edu.my/7554/2/ATTIKA%20AHMED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/7554/3/ATTIKA%20AHMED%20WATERMARK.pdf
_version_	1747831167572246528

An improved self organizing map using jaccard new measure for textual bugs data clustering

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