Improved Parameterless K-Means : Auto-Generation Centroids and Distance Data Point Clusters
K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mohd, Wan Maseri Binti Wan [verfasserIn] Beg, A.H. [author] Herawan, Tutut [author] Noraziah, A. [author] Rabbi, K. F. [author] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2011 |
|---|
| Schlagwörter: |
|---|
| Umfang: |
Online-Ressource |
|---|
| Reproduktion: |
IGI Global InfoSci Journals Archive 2000 - 2012 |
|---|---|
| Übergeordnetes Werk: |
In: International journal of information retrieval research - Hershey, Pa : IGI Global, 2011, 1(2011), 3, Seite 1-14 |
| Übergeordnetes Werk: |
volume:1 ; year:2011 ; number:3 ; pages:1-14 |
| Links: |
|---|
| DOI / URN: |
10.4018/ijirr.2011070101 |
|---|
| Katalog-ID: |
NLEJ244481202 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLEJ244481202 | ||
| 003 | DE-627 | ||
| 005 | 20240202180158.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 150605s2011 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.4018/ijirr.2011070101 |2 doi | |
| 035 | |a (DE-627)NLEJ244481202 | ||
| 035 | |a (VZGNL)10.4018/ijirr.2011070101 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Mohd, Wan Maseri Binti Wan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improved Parameterless K-Means |b Auto-Generation Centroids and Distance Data Point Clusters |
| 264 | 1 | |c 2011 | |
| 300 | |a Online-Ressource | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points | ||
| 533 | |f IGI Global InfoSci Journals Archive 2000 - 2012 | ||
| 650 | 4 | |a Clustering | |
| 650 | 4 | |a Clustering Process | |
| 650 | 4 | |a Data Mining | |
| 650 | 4 | |a K-Means Algorithm | |
| 650 | 4 | |a Partitioning Clustering Algorithm | |
| 700 | 1 | |a Beg, A.H. |e author |4 aut | |
| 700 | 1 | |a Herawan, Tutut |e author |4 aut | |
| 700 | 1 | |a Noraziah, A. |e author |4 oth | |
| 700 | 1 | |a Rabbi, K. F. |e author |4 oth | |
| 773 | 0 | 8 | |i In |t International journal of information retrieval research |d Hershey, Pa : IGI Global, 2011 |g 1(2011), 3, Seite 1-14 |h Online-Ressource |w (DE-627)NLEJ244419159 |w (DE-600)2703390-9 |x 2155-6385 |7 nnns |
| 773 | 1 | 8 | |g volume:1 |g year:2011 |g number:3 |g pages:1-14 |
| 856 | 4 | 0 | |u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101 |m X:IGIG |x Verlag |z Deutschlandweit zugänglich |
| 856 | 4 | 2 | |u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true |q text/html |y Abstract |z Deutschlandweit zugänglich |
| 912 | |a ZDB-1-GIS | ||
| 912 | |a GBV_NL_ARTICLE | ||
| 951 | |a AR | ||
| 952 | |d 1 |j 2011 |e 3 |h 1-14 | ||
| author_variant |
w m b w m wmbw wmbwm a b ab t h th a n an k f r kf kfr |
|---|---|
| matchkey_str |
article:21556385:2011----::mrvdaaeel |
| hierarchy_sort_str |
2011 |
| publishDate |
2011 |
| allfields |
10.4018/ijirr.2011070101 doi (DE-627)NLEJ244481202 (VZGNL)10.4018/ijirr.2011070101 DE-627 ger DE-627 rakwb eng Mohd, Wan Maseri Binti Wan verfasserin aut Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points IGI Global InfoSci Journals Archive 2000 - 2012 Clustering Clustering Process Data Mining K-Means Algorithm Partitioning Clustering Algorithm Beg, A.H. author aut Herawan, Tutut author aut Noraziah, A. author oth Rabbi, K. F. author oth In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 1(2011), 3, Seite 1-14 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:1 year:2011 number:3 pages:1-14 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 1 2011 3 1-14 |
| spelling |
10.4018/ijirr.2011070101 doi (DE-627)NLEJ244481202 (VZGNL)10.4018/ijirr.2011070101 DE-627 ger DE-627 rakwb eng Mohd, Wan Maseri Binti Wan verfasserin aut Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points IGI Global InfoSci Journals Archive 2000 - 2012 Clustering Clustering Process Data Mining K-Means Algorithm Partitioning Clustering Algorithm Beg, A.H. author aut Herawan, Tutut author aut Noraziah, A. author oth Rabbi, K. F. author oth In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 1(2011), 3, Seite 1-14 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:1 year:2011 number:3 pages:1-14 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 1 2011 3 1-14 |
| allfields_unstemmed |
10.4018/ijirr.2011070101 doi (DE-627)NLEJ244481202 (VZGNL)10.4018/ijirr.2011070101 DE-627 ger DE-627 rakwb eng Mohd, Wan Maseri Binti Wan verfasserin aut Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points IGI Global InfoSci Journals Archive 2000 - 2012 Clustering Clustering Process Data Mining K-Means Algorithm Partitioning Clustering Algorithm Beg, A.H. author aut Herawan, Tutut author aut Noraziah, A. author oth Rabbi, K. F. author oth In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 1(2011), 3, Seite 1-14 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:1 year:2011 number:3 pages:1-14 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 1 2011 3 1-14 |
| allfieldsGer |
10.4018/ijirr.2011070101 doi (DE-627)NLEJ244481202 (VZGNL)10.4018/ijirr.2011070101 DE-627 ger DE-627 rakwb eng Mohd, Wan Maseri Binti Wan verfasserin aut Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points IGI Global InfoSci Journals Archive 2000 - 2012 Clustering Clustering Process Data Mining K-Means Algorithm Partitioning Clustering Algorithm Beg, A.H. author aut Herawan, Tutut author aut Noraziah, A. author oth Rabbi, K. F. author oth In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 1(2011), 3, Seite 1-14 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:1 year:2011 number:3 pages:1-14 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 1 2011 3 1-14 |
| allfieldsSound |
10.4018/ijirr.2011070101 doi (DE-627)NLEJ244481202 (VZGNL)10.4018/ijirr.2011070101 DE-627 ger DE-627 rakwb eng Mohd, Wan Maseri Binti Wan verfasserin aut Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters 2011 Online-Ressource nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points IGI Global InfoSci Journals Archive 2000 - 2012 Clustering Clustering Process Data Mining K-Means Algorithm Partitioning Clustering Algorithm Beg, A.H. author aut Herawan, Tutut author aut Noraziah, A. author oth Rabbi, K. F. author oth In International journal of information retrieval research Hershey, Pa : IGI Global, 2011 1(2011), 3, Seite 1-14 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:1 year:2011 number:3 pages:1-14 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true text/html Abstract Deutschlandweit zugänglich ZDB-1-GIS GBV_NL_ARTICLE AR 1 2011 3 1-14 |
| language |
English |
| source |
In International journal of information retrieval research 1(2011), 3, Seite 1-14 volume:1 year:2011 number:3 pages:1-14 |
| sourceStr |
In International journal of information retrieval research 1(2011), 3, Seite 1-14 volume:1 year:2011 number:3 pages:1-14 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Clustering Clustering Process Data Mining K-Means Algorithm Partitioning Clustering Algorithm |
| isfreeaccess_bool |
false |
| container_title |
International journal of information retrieval research |
| authorswithroles_txt_mv |
Mohd, Wan Maseri Binti Wan @@aut@@ Beg, A.H. @@aut@@ Herawan, Tutut @@aut@@ Noraziah, A. @@oth@@ Rabbi, K. F. @@oth@@ |
| publishDateDaySort_date |
2011-01-01T00:00:00Z |
| hierarchy_top_id |
NLEJ244419159 |
| id |
NLEJ244481202 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ244481202</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240202180158.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">150605s2011 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/ijirr.2011070101</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ244481202</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/ijirr.2011070101</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">Mohd, Wan Maseri Binti Wan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improved Parameterless K-Means</subfield><subfield code="b">Auto-Generation Centroids and Distance Data Point Clusters</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</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">K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">IGI Global InfoSci Journals Archive 2000 - 2012</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering Process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data Mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-Means Algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partitioning Clustering Algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Beg, A.H.</subfield><subfield code="e">author</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Herawan, Tutut</subfield><subfield code="e">author</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Noraziah, A.</subfield><subfield code="e">author</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rabbi, K. F.</subfield><subfield code="e">author</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International journal of information retrieval research</subfield><subfield code="d">Hershey, Pa : IGI Global, 2011</subfield><subfield code="g">1(2011), 3, Seite 1-14</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ244419159</subfield><subfield code="w">(DE-600)2703390-9</subfield><subfield code="x">2155-6385</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:1</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:1-14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true</subfield><subfield code="q">text/html</subfield><subfield code="y">Abstract</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">1</subfield><subfield code="j">2011</subfield><subfield code="e">3</subfield><subfield code="h">1-14</subfield></datafield></record></collection>
|
| series2 |
IGI Global InfoSci Journals Archive 2000 - 2012 |
| author |
Mohd, Wan Maseri Binti Wan |
| spellingShingle |
Mohd, Wan Maseri Binti Wan misc Clustering misc Clustering Process misc Data Mining misc K-Means Algorithm misc Partitioning Clustering Algorithm Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters |
| authorStr |
Mohd, Wan Maseri Binti Wan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)NLEJ244419159 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut oth oth |
| collection |
NL |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
2155-6385 |
| topic_title |
Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters Clustering Clustering Process Data Mining K-Means Algorithm Partitioning Clustering Algorithm |
| topic |
misc Clustering misc Clustering Process misc Data Mining misc K-Means Algorithm misc Partitioning Clustering Algorithm |
| topic_unstemmed |
misc Clustering misc Clustering Process misc Data Mining misc K-Means Algorithm misc Partitioning Clustering Algorithm |
| topic_browse |
misc Clustering misc Clustering Process misc Data Mining misc K-Means Algorithm misc Partitioning Clustering Algorithm |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
a n an k f r kf kfr |
| hierarchy_parent_title |
International journal of information retrieval research |
| hierarchy_parent_id |
NLEJ244419159 |
| hierarchy_top_title |
International journal of information retrieval research |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)NLEJ244419159 (DE-600)2703390-9 |
| title |
Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters |
| ctrlnum |
(DE-627)NLEJ244481202 (VZGNL)10.4018/ijirr.2011070101 |
| title_full |
Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters |
| author_sort |
Mohd, Wan Maseri Binti Wan |
| journal |
International journal of information retrieval research |
| journalStr |
International journal of information retrieval research |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2011 |
| contenttype_str_mv |
zzz |
| container_start_page |
1 |
| author_browse |
Mohd, Wan Maseri Binti Wan Beg, A.H. Herawan, Tutut |
| container_volume |
1 |
| physical |
Online-Ressource |
| format_se |
Elektronische Aufsätze |
| author-letter |
Mohd, Wan Maseri Binti Wan |
| title_sub |
Auto-Generation Centroids and Distance Data Point Clusters |
| doi_str_mv |
10.4018/ijirr.2011070101 |
| author2-role |
author |
| title_sort |
improved parameterless k-meansauto-generation centroids and distance data point clusters |
| title_auth |
Improved Parameterless K-Means Auto-Generation Centroids and Distance Data Point Clusters |
| abstract |
K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points |
| abstractGer |
K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points |
| abstract_unstemmed |
K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points |
| collection_details |
ZDB-1-GIS GBV_NL_ARTICLE |
| container_issue |
3 |
| title_short |
Improved Parameterless K-Means |
| url |
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true |
| remote_bool |
true |
| author2 |
Beg, A.H. Herawan, Tutut Noraziah, A. Rabbi, K. F. |
| author2Str |
Beg, A.H. Herawan, Tutut Noraziah, A. Rabbi, K. F. |
| ppnlink |
NLEJ244419159 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.4018/ijirr.2011070101 |
| up_date |
2024-07-06T08:02:35.153Z |
| _version_ |
1803815962703560704 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ244481202</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240202180158.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">150605s2011 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/ijirr.2011070101</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ244481202</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/ijirr.2011070101</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">Mohd, Wan Maseri Binti Wan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improved Parameterless K-Means</subfield><subfield code="b">Auto-Generation Centroids and Distance Data Point Clusters</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</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">K-means is an unsupervised learning and partitioning clustering algorithm. It is popular and widely used for its simplicity and fastness. K-means clustering produce a number of separate flat (non-hierarchical) clusters and suitable for generating globular clusters. The main drawback of the k-means algorithm is that the user must specify the number of clusters in advance. This paper presents an improved version of K-means algorithm with auto-generate an initial number of clusters (k) and a new approach of defining initial Centroid for effective and efficient clustering process. The underlined mechanism has been analyzed and experimented. The experimental results show that the number of iteration is reduced to 50% and the run time is lower and constantly based on maximum distance of data points, regardless of how many data points</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">IGI Global InfoSci Journals Archive 2000 - 2012</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering Process</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data Mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-Means Algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Partitioning Clustering Algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Beg, A.H.</subfield><subfield code="e">author</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Herawan, Tutut</subfield><subfield code="e">author</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Noraziah, A.</subfield><subfield code="e">author</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rabbi, K. F.</subfield><subfield code="e">author</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">International journal of information retrieval research</subfield><subfield code="d">Hershey, Pa : IGI Global, 2011</subfield><subfield code="g">1(2011), 3, Seite 1-14</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ244419159</subfield><subfield code="w">(DE-600)2703390-9</subfield><subfield code="x">2155-6385</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:1</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:1-14</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijirr.2011070101&buylink=true</subfield><subfield code="q">text/html</subfield><subfield code="y">Abstract</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">1</subfield><subfield code="j">2011</subfield><subfield code="e">3</subfield><subfield code="h">1-14</subfield></datafield></record></collection>
|
| score |
7.4031916 |