Hamming Distance based Clustering Algorithm
Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vijay, Ritu [verfasserIn] Mahajan, Prerna [author] Kandwal, Rekha [author] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Umfang: |
Online-Ressource |
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| Reproduktion: |
IGI Global InfoSci Journals Archive 2000 - 2012 |
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| Übergeordnetes Werk: |
In: International journal of information retrieval research - Hershey, Pa : IGI Global, 2011, 2(2012), 1, Seite 11-20 |
| Übergeordnetes Werk: |
volume:2 ; year:2012 ; number:1 ; pages:11-20 |
| Links: |
|---|
| DOI / URN: |
10.4018/ijirr.2012010102 |
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NLEJ244481296 |
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| 520 | |a Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data | ||
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10.4018/ijirr.2012010102 doi (DE-627)NLEJ244481296 (VZGNL)10.4018/ijirr.2012010102 DE-627 ger DE-627 rakwb eng Vijay, Ritu verfasserin aut Hamming Distance based Clustering Algorithm 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data IGI Global InfoSci Journals Archive 2000 - 2012 Categorical Data Clustering Distance Metric Hamming Distance K-Means Mahajan, Prerna author aut Kandwal, Rekha author aut In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 2(2012), 1, Seite 11-20 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:2 year:2012 number:1 pages:11-20 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2012 1 11-20 |
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10.4018/ijirr.2012010102 doi (DE-627)NLEJ244481296 (VZGNL)10.4018/ijirr.2012010102 DE-627 ger DE-627 rakwb eng Vijay, Ritu verfasserin aut Hamming Distance based Clustering Algorithm 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data IGI Global InfoSci Journals Archive 2000 - 2012 Categorical Data Clustering Distance Metric Hamming Distance K-Means Mahajan, Prerna author aut Kandwal, Rekha author aut In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 2(2012), 1, Seite 11-20 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:2 year:2012 number:1 pages:11-20 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2012 1 11-20 |
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10.4018/ijirr.2012010102 doi (DE-627)NLEJ244481296 (VZGNL)10.4018/ijirr.2012010102 DE-627 ger DE-627 rakwb eng Vijay, Ritu verfasserin aut Hamming Distance based Clustering Algorithm 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data IGI Global InfoSci Journals Archive 2000 - 2012 Categorical Data Clustering Distance Metric Hamming Distance K-Means Mahajan, Prerna author aut Kandwal, Rekha author aut In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 2(2012), 1, Seite 11-20 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:2 year:2012 number:1 pages:11-20 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2012 1 11-20 |
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10.4018/ijirr.2012010102 doi (DE-627)NLEJ244481296 (VZGNL)10.4018/ijirr.2012010102 DE-627 ger DE-627 rakwb eng Vijay, Ritu verfasserin aut Hamming Distance based Clustering Algorithm 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data IGI Global InfoSci Journals Archive 2000 - 2012 Categorical Data Clustering Distance Metric Hamming Distance K-Means Mahajan, Prerna author aut Kandwal, Rekha author aut In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 2(2012), 1, Seite 11-20 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:2 year:2012 number:1 pages:11-20 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2012 1 11-20 |
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10.4018/ijirr.2012010102 doi (DE-627)NLEJ244481296 (VZGNL)10.4018/ijirr.2012010102 DE-627 ger DE-627 rakwb eng Vijay, Ritu verfasserin aut Hamming Distance based Clustering Algorithm 2012 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data IGI Global InfoSci Journals Archive 2000 - 2012 Categorical Data Clustering Distance Metric Hamming Distance K-Means Mahajan, Prerna author aut Kandwal, Rekha author aut In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 2(2012), 1, Seite 11-20 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:2 year:2012 number:1 pages:11-20 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2012010102&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 2 2012 1 11-20 |
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Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data |
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Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data |
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Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups. In this paper the authors have extended the concept of hamming distance for categorical data .As a data processing step they have transformed the data into binary representation. The authors have used proposed algorithm to group data points into clusters. The experiments are carried out on the data sets from UCI machine learning repository to analyze the performance study. They conclude by stating that this proposed algorithm shows promising result and can be extended to handle numeric as well as mixed data |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ244481296</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240202180158.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">150605s2012 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/ijirr.2012010102</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ244481296</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/ijirr.2012010102</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vijay, Ritu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hamming Distance based Clustering Algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. 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