Muutke küpsiste eelistusi

E-raamat: Large-Scale Parallel Data Mining

4.50/5 (4 hinnangut Goodreads-ist)
Edited by , Edited by
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 55,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Large-Scale Parallel Data MiningSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Withthe unprecedented rate at which data is being collected today in almostall elds of human endeavor, there is an emerging economic and scientic need to extract useful information from it. For example, many companies already have data-warehouses inthe terabyte range (e.g., FedEx, Walmart).The WorldWide Web has an estimated 800 millionweb-pages. Similarly,scienti c data is rea- ing gigantic proportions (e.g., NASA space missions, Human Genome Project). High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalabilityand interactivityas datasets continue to grow in size and complexity. Toaddress thisneedweorganizedtheworkshoponLarge-ScaleParallelKDD Systems, which was held in conjunction with the 5th ACM SIGKDD Inter- tional Conference on Knowledge Discovery and Data Mining, on August 15th, 1999, San Diego, California. The goal of this workshop was to bring researchers and practitioners together in a setting where they could discuss the design, - plementation,anddeploymentoflarge-scaleparallelknowledgediscovery (PKD) systems, which can manipulate data taken from very large enterprise or sci- tic databases, regardless of whether the data is located centrally or is globally distributed. Relevant topics identie d for the workshop included: { How to develop a rapid-response, scalable, and parallel knowledge discovery system that supports global organizations with terabytes of data.

Muu info

Springer Book Archives
Large-Scale Parallel Data Mining Parallel and Distributed Data Mining: An Introduction 1(23) Mohammed J. Zaki Mining Frameworks The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project 24(31) Graham Williams Irfan Altas Sergey Bakin Peter Christen Markus Hegland Alonso Marquez Peter Milne Rajehndra Nagappan Stephen Roberts A High Performance Implementation of the Data Space Transfer Protocol (DSTP) 55(10) Stuart Bailey Emory Creel Robert Grossman Srinath Gutti Harinath Sivakumar Active Mining in a Distributed Setting 65(18) Srinivasan Parthasarathy Sandhya Dwarkadas Mitsunori Ogihara Associations and Sequences Efficient Parallel Algorithms for Mining Associations 83(44) Mahesh V. Joshi Eui-Hong (Sam) Han George Karypis Vipin Kumar Parallel Branch-and-Bound Graph Search for Correlated Association Rules 127(18) Shinichi Morishita Akihiro Nakaya Parallel Generalized Association Rule Mining on Large Scale PC Cluster 145(16) Takahiko Shintani Masaru Kitsuregawa Parallel Sequence Mining on Shared-Memory Machines 161(29) Mohammed J. Zaki Classification Parallel Predictor Generation 190(7) D.B. Skillicorn Efficient Parallel Classification Using Dimensional Aggregates 197(14) Sanjay Goil Alok Choudhary Learning Rules from Distributed Data 211(10) Lawrence O. Hall Nitesh Chawala Kevin W. Bowyer W. Philip Kegelmeyer Clustering Collective, Hierarchical Clustering from Distributed, Heterogeneous Data 221(24) Erik L. Johnson Hillol Kargupta A Data-Cluster Algorithm On Distributed Memory Multiprocessors 245(16) Inderjit S. Dhillon Dharmendra S. Modha Author Index 261
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97835404650272e.html
Märksõnad: