A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures

Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical stor...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jiang, Xiaoping [verfasserIn] Li, Chenghua [verfasserIn] Sun, Jing [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Multimedia data bases Clustering Mining K means clustering Optimization

Übergeordnetes Werk:	Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 21(2017), 1 vom: 02. Juni, Seite 797-804
Übergeordnetes Werk:	volume:21 ; year:2017 ; number:1 ; day:02 ; month:06 ; pages:797-804

Links:	Volltext

DOI / URN:	10.1007/s10586-017-0949-6

Katalog-ID:	SPR01150871X

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520			\|a Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database.
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allfields	10.1007/s10586-017-0949-6 doi (DE-627)SPR01150871X (SPR)s10586-017-0949-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Jiang, Xiaoping verfasserin aut A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database. Multimedia data bases (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Mining (dpeaa)DE-He213 K means clustering (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Li, Chenghua verfasserin aut Sun, Jing verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 21(2017), 1 vom: 02. Juni, Seite 797-804 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:21 year:2017 number:1 day:02 month:06 pages:797-804 https://dx.doi.org/10.1007/s10586-017-0949-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 21 2017 1 02 06 797-804
spelling	10.1007/s10586-017-0949-6 doi (DE-627)SPR01150871X (SPR)s10586-017-0949-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Jiang, Xiaoping verfasserin aut A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database. Multimedia data bases (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Mining (dpeaa)DE-He213 K means clustering (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Li, Chenghua verfasserin aut Sun, Jing verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 21(2017), 1 vom: 02. Juni, Seite 797-804 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:21 year:2017 number:1 day:02 month:06 pages:797-804 https://dx.doi.org/10.1007/s10586-017-0949-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 21 2017 1 02 06 797-804
allfields_unstemmed	10.1007/s10586-017-0949-6 doi (DE-627)SPR01150871X (SPR)s10586-017-0949-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Jiang, Xiaoping verfasserin aut A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database. Multimedia data bases (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Mining (dpeaa)DE-He213 K means clustering (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Li, Chenghua verfasserin aut Sun, Jing verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 21(2017), 1 vom: 02. Juni, Seite 797-804 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:21 year:2017 number:1 day:02 month:06 pages:797-804 https://dx.doi.org/10.1007/s10586-017-0949-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 21 2017 1 02 06 797-804
allfieldsGer	10.1007/s10586-017-0949-6 doi (DE-627)SPR01150871X (SPR)s10586-017-0949-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Jiang, Xiaoping verfasserin aut A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database. Multimedia data bases (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Mining (dpeaa)DE-He213 K means clustering (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Li, Chenghua verfasserin aut Sun, Jing verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 21(2017), 1 vom: 02. Juni, Seite 797-804 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:21 year:2017 number:1 day:02 month:06 pages:797-804 https://dx.doi.org/10.1007/s10586-017-0949-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 21 2017 1 02 06 797-804
allfieldsSound	10.1007/s10586-017-0949-6 doi (DE-627)SPR01150871X (SPR)s10586-017-0949-6-e DE-627 ger DE-627 rakwb eng 004 ASE 54.50 bkl 54.32 bkl 54.25 bkl Jiang, Xiaoping verfasserin aut A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database. Multimedia data bases (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Mining (dpeaa)DE-He213 K means clustering (dpeaa)DE-He213 Optimization (dpeaa)DE-He213 Li, Chenghua verfasserin aut Sun, Jing verfasserin aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 21(2017), 1 vom: 02. Juni, Seite 797-804 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:21 year:2017 number:1 day:02 month:06 pages:797-804 https://dx.doi.org/10.1007/s10586-017-0949-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.50 ASE 54.32 ASE 54.25 ASE AR 21 2017 1 02 06 797-804
language	English
source	Enthalten in Cluster computing 21(2017), 1 vom: 02. Juni, Seite 797-804 volume:21 year:2017 number:1 day:02 month:06 pages:797-804
sourceStr	Enthalten in Cluster computing 21(2017), 1 vom: 02. Juni, Seite 797-804 volume:21 year:2017 number:1 day:02 month:06 pages:797-804
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Multimedia data bases Clustering Mining K means clustering Optimization
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container_title	Cluster computing
authorswithroles_txt_mv	Jiang, Xiaoping @@aut@@ Li, Chenghua @@aut@@ Sun, Jing @@aut@@
publishDateDaySort_date	2017-06-02T00:00:00Z
hierarchy_top_id	320505332
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language_de	englisch
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Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. 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author	Jiang, Xiaoping
spellingShingle	Jiang, Xiaoping ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Multimedia data bases misc Clustering misc Mining misc K means clustering misc Optimization A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures
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topic_title	004 ASE 54.50 bkl 54.32 bkl 54.25 bkl A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures Multimedia data bases (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Mining (dpeaa)DE-He213 K means clustering (dpeaa)DE-He213 Optimization (dpeaa)DE-He213
topic	ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Multimedia data bases misc Clustering misc Mining misc K means clustering misc Optimization
topic_unstemmed	ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Multimedia data bases misc Clustering misc Mining misc K means clustering misc Optimization
topic_browse	ddc 004 bkl 54.50 bkl 54.32 bkl 54.25 misc Multimedia data bases misc Clustering misc Mining misc K means clustering misc Optimization
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title	A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures
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title_full	A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures
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title_sort	modified k-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures
title_auth	A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures
abstract	Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database.
abstractGer	Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database.
abstract_unstemmed	Abstract With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database.
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title_short	A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures
url	https://dx.doi.org/10.1007/s10586-017-0949-6
remote_bool	true
author2	Li, Chenghua Sun, Jing
author2Str	Li, Chenghua Sun, Jing
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doi_str	10.1007/s10586-017-0949-6
up_date	2024-07-03T23:04:56.156Z
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A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures

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