a generic local algorithm for mining data streams in large distributed systems in philippines

Computing global data mining models eg decision trees kmeans clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost which may be high The cost further increases in a dynamic scenario when the data changes rapidly...We are a professional mining machinery manufacturer, the main equipment including: jaw crusher, cone crusher and other sandstone equipment;Ball mill, flotation machine, concentrator and other beneficiation equipment; Powder Grinding Plant, rotary dryer, briquette machine, mining, metallurgy and other related equipment. which can crush all kinds of metal and non-metallic ore, also can be dry grinding and wet grinding.If you are interested in our products or want to visit the nearby production site, you can click the button below to consult us.Welcome to our factory to test machine for free!

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  • A Generic Local Algorithm for Mining Data Streams in Large

    A Generic Local Algorithm for Mining Data Streams in Large

    Computing global data mining models eg decision trees kmeans clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost which may be high The cost further increases in a dynamic scenario when the data changes rapidly

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  • PDF Towards Data Mining in Large and Fully Distributed

    PDF Towards Data Mining in Large and Fully Distributed

    The Internet which is becoming a more and more dynamic extremely heterogeneous network has recently became a platform for huge fully distributed peertopeer overlay networks containing millions of nodes typically for the purpose of information dissemination and le sharing This paper targets the problem of analyzing data which are scattered over a such huge and dynamic set of nodes where

    Details >
  • PDF Collective Sequential Pattern Mining in Distributed

    PDF Collective Sequential Pattern Mining in Distributed

    14 K Bhaduri and H Kargupta A Generic Local Algorithm for Mining Data St re ams in Large Distributed Systems IEEE Transactions on Knowledge and Data Eng

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  • A Generic Local Algorithm for Mining Data Streams in Large

    A Generic Local Algorithm for Mining Data Streams in Large

    Computing global data mining models eg decision trees kmeans clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost which may be high The cost further increases in a dynamic scenario when the data changes rapidly

    Details >
  • 1A Generic Local Algorithm for Mining Data Streams in

    1A Generic Local Algorithm for Mining Data Streams in

    BibTeX MISCWolff1ageneric author Ran Wolff and Kanishka Bhaduri and Hillol Kargupta and Senior Member title 1A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems year

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  • PDF Towards Data Mining in Large and Fully Distributed

    PDF Towards Data Mining in Large and Fully Distributed

    The Internet which is becoming a more and more dynamic extremely heterogeneous network has recently became a platform for huge fully distributed peertopeer overlay networks containing millions of nodes typically for the purpose of information dissemination and le sharing This paper targets the problem of analyzing data which are scattered over a such huge and dynamic set of nodes where

    Details >
  • Lightweight Monitoring of Distributed Streams  ACM

    Lightweight Monitoring of Distributed Streams ACM

    Ran Wolff Kanishka Bhaduri and Hillol Kargupta 2009 A generic local algorithm for mining data streams in large distributed systems TKDE 21 4 2009 465478 Google Scholar Digital Library James Yeh 2006 Real Analysis Theory of Measure and Integration Second Edition World Scientific Publishing Company Google Scholar Cross Ref

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  • PDF Collective Sequential Pattern Mining in Distributed

    PDF Collective Sequential Pattern Mining in Distributed

    14 K Bhaduri and H Kargupta A Generic Local Algorithm for Mining Data St re ams in Large Distributed Systems IEEE Transactions on Knowledge and Data Eng

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  • UMBC Dr Hillol Kargupta

    UMBC Dr Hillol Kargupta

    A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems IEEE Transactions on Knowledge and Data Engineering Volume 21 Issue 4 pp 465478

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  • Ran Wolff  Google Scholar Citations

    Ran Wolff Google Scholar Citations

    This Cited by count includes citations to the following articles in Scholar The ones marked may be different from the article in the profile

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  • Madhuri Nagaraj Kaushik  Arizona State University  San

    Madhuri Nagaraj Kaushik Arizona State University San

    A Generic Local Algorithm for Routing and Mining Data Streams in Large Distributed Systems Jan 2010 – May 2010 An algorithm was applied to the scenario of a dynamic network to optimize

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  • ContextAdaptive Big Data Stream Mining  Online

    ContextAdaptive Big Data Stream Mining Online

    The data streams collected by such sources are heterogeneous and dynamically evolving over time in unknown and unpredictable ways Hence mining these data streams online at runtime is known to be a very challenging problem 5 6 For instance it is wellknown that such online stream mining problems need to cope with concept drift 27

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