Research on internet information mining based on agent algorithm
With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this pape...
Ausführliche Beschreibung
Autor*in: |
Wu, Shaofei [verfasserIn] Wang, Mingqing [verfasserIn] Zou, Yuntao [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Future generation computer systems - Amsterdam [u.a.] : Elsevier Science, 1984, 86, Seite 598-602 |
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Übergeordnetes Werk: |
volume:86 ; pages:598-602 |
DOI / URN: |
10.1016/j.future.2018.04.040 |
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Katalog-ID: |
ELV000204919 |
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520 | |a With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. | ||
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10.1016/j.future.2018.04.040 doi (DE-627)ELV000204919 (ELSEVIER)S0167-739X(18)30290-5 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Wu, Shaofei verfasserin aut Research on internet information mining based on agent algorithm 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. Agent algorithm Intelligence Data mining Wang, Mingqing verfasserin aut Zou, Yuntao verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 86, Seite 598-602 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:86 pages:598-602 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 86 598-602 |
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10.1016/j.future.2018.04.040 doi (DE-627)ELV000204919 (ELSEVIER)S0167-739X(18)30290-5 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Wu, Shaofei verfasserin aut Research on internet information mining based on agent algorithm 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. Agent algorithm Intelligence Data mining Wang, Mingqing verfasserin aut Zou, Yuntao verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 86, Seite 598-602 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:86 pages:598-602 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 86 598-602 |
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10.1016/j.future.2018.04.040 doi (DE-627)ELV000204919 (ELSEVIER)S0167-739X(18)30290-5 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Wu, Shaofei verfasserin aut Research on internet information mining based on agent algorithm 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. Agent algorithm Intelligence Data mining Wang, Mingqing verfasserin aut Zou, Yuntao verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 86, Seite 598-602 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:86 pages:598-602 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 86 598-602 |
allfieldsGer |
10.1016/j.future.2018.04.040 doi (DE-627)ELV000204919 (ELSEVIER)S0167-739X(18)30290-5 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Wu, Shaofei verfasserin aut Research on internet information mining based on agent algorithm 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. Agent algorithm Intelligence Data mining Wang, Mingqing verfasserin aut Zou, Yuntao verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 86, Seite 598-602 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:86 pages:598-602 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 86 598-602 |
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10.1016/j.future.2018.04.040 doi (DE-627)ELV000204919 (ELSEVIER)S0167-739X(18)30290-5 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Wu, Shaofei verfasserin aut Research on internet information mining based on agent algorithm 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. Agent algorithm Intelligence Data mining Wang, Mingqing verfasserin aut Zou, Yuntao verfasserin aut Enthalten in Future generation computer systems Amsterdam [u.a.] : Elsevier Science, 1984 86, Seite 598-602 Online-Ressource (DE-627)320604284 (DE-600)2020551-X (DE-576)094399212 0167-739X nnns volume:86 pages:598-602 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4338 GBV_ILN_4393 54.00 Informatik: Allgemeines VZ AR 86 598-602 |
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authorswithroles_txt_mv |
Wu, Shaofei @@aut@@ Wang, Mingqing @@aut@@ Zou, Yuntao @@aut@@ |
publishDateDaySort_date |
2018-01-01T00:00:00Z |
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320604284 |
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id |
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Wu, Shaofei ddc 004 bkl 54.00 misc Agent algorithm misc Intelligence misc Data mining Research on internet information mining based on agent algorithm |
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research on internet information mining based on agent algorithm |
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Research on internet information mining based on agent algorithm |
abstract |
With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. |
abstractGer |
With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. |
abstract_unstemmed |
With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified. |
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Research on internet information mining based on agent algorithm |
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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">ELV000204919</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231002073011.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230427s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.future.2018.04.040</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV000204919</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0167-739X(18)30290-5</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Shaofei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Research on internet information mining based on agent algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Agent algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data mining</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Mingqing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zou, Yuntao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Future generation computer systems</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1984</subfield><subfield code="g">86, Seite 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