Parallel K Means Algorithm Based by Lopes Veloso (10 results)
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
Contact seller4-star sellerCondition: New
£ 39.46
Free ShippingShips within U.S.A.Quantity: Over 20 available
Condition: New.
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
Contact seller5-star sellerCondition: New
£ 36.11
£ 3.29 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 67.15
£ 3.00 shippingShips within U.S.A.Quantity: 4 available
Condition: New.
- More images
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
£ 32.00
£ 59.82 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: Neu. Parallel K-Means Algorithm based on Hadoop-MapReduce for Mining | Lays Helena Lopes Veloso (u. a.) | Taschenbuch | Englisch | 2025 | Our Knowledge Publishing | EAN 9786209114083 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[…at]vdm-vsg[dot]de | Anbieter: preigu.
- More images
Language: English
Published by Omniscriptum, Our Knowledge Publishing Okt 2025, 2025
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 35.12
£ 19.66 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp…and ScaleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests. 56 pp. Englisch.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 64.39
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 66.17
£ 8.50 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 39.99
£ 37.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and ScaleUp. To this end, experiments were performed on a Ha…doop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- More images
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 35.12
£ 51.28 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and…ScaleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.
- More images
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 36.00
£ 51.71 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and S…caleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests.
