Software component clustering and classification using novel similarity measure.
Saved in:
| Title: | Software component clustering and classification using novel similarity measure. |
|---|---|
| Authors: | Srinivas, Chintakindi1, Radhakrishna, Vangipuram2 radhakrishna_v@vnrvjiet.in, Rao, C. V.Guru3 |
| Source: | Interdisciplinarity in Engineering. 2015, Vol. 19, p866-873. 8p. |
| Subjects: | Computer software research, Cluster analysis (Statistics), Similarity transformations, Gaussian function, Euclidean algorithm, Cosine function |
| Abstract: | The similarity measures such as Euclidean, Jaccard, Cosine, Manhattan etc present in the literature only consider the count of the features but does not consider the feature distribution and the degree of commonality. There is a significant research carried out for designing new similarity measures which can accurately find the similarity between any two software components. The distribution of component features in the software components has important contribution in evaluating their degree of similarity. This is the Key idea for the design of the proposed measure. The main objective of this research is to first design an efficient similarity measure which essentially considers the distribution of the features over the entire input. We then carry out the analysis for worst case, average case and best case situations. The proposed measure is Gaussian based and preserves the properties of Gaussian function and can be used for clustering and classification of software components. [ABSTRACT FROM AUTHOR] |
| Copyright of Interdisciplinarity in Engineering is the property of University of Medicine, Pharmacy, Sciences and Technology of Tîrgu Mures and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 102706901 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Software component clustering and classification using novel similarity measure. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Srinivas%2C+Chintakindi%22">Srinivas, Chintakindi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Radhakrishna%2C+Vangipuram%22">Radhakrishna, Vangipuram</searchLink><relatesTo>2</relatesTo><i> radhakrishna_v@vnrvjiet.in</i><br /><searchLink fieldCode="AR" term="%22Rao%2C+C%2E+V%2EGuru%22">Rao, C. V.Guru</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Interdisciplinarity+in+Engineering%22">Interdisciplinarity in Engineering</searchLink>. 2015, Vol. 19, p866-873. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+software+research%22">Computer software research</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Similarity+transformations%22">Similarity transformations</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+function%22">Gaussian function</searchLink><br /><searchLink fieldCode="DE" term="%22Euclidean+algorithm%22">Euclidean algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Cosine+function%22">Cosine function</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The similarity measures such as Euclidean, Jaccard, Cosine, Manhattan etc present in the literature only consider the count of the features but does not consider the feature distribution and the degree of commonality. There is a significant research carried out for designing new similarity measures which can accurately find the similarity between any two software components. The distribution of component features in the software components has important contribution in evaluating their degree of similarity. This is the Key idea for the design of the proposed measure. The main objective of this research is to first design an efficient similarity measure which essentially considers the distribution of the features over the entire input. We then carry out the analysis for worst case, average case and best case situations. The proposed measure is Gaussian based and preserves the properties of Gaussian function and can be used for clustering and classification of software components. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Interdisciplinarity in Engineering is the property of University of Medicine, Pharmacy, Sciences and Technology of Tîrgu Mures and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=102706901 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.protcy.2015.02.124 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 866 Subjects: – SubjectFull: Computer software research Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Similarity transformations Type: general – SubjectFull: Gaussian function Type: general – SubjectFull: Euclidean algorithm Type: general – SubjectFull: Cosine function Type: general Titles: – TitleFull: Software component clustering and classification using novel similarity measure. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Srinivas, Chintakindi – PersonEntity: Name: NameFull: Radhakrishna, Vangipuram – PersonEntity: Name: NameFull: Rao, C. V.Guru IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 22850945 Numbering: – Type: volume Value: 19 Titles: – TitleFull: Interdisciplinarity in Engineering Type: main |
| ResultId | 1 |
