標題: Titlebook: Compression Schemes for Mining Large Datasets; A Machine Learning P T. Ravindra Babu,M. Narasimha Murty,S.V. Subrahman Book 2013 Springer-V [打印本頁] 作者: 平凡人 時間: 2025-3-21 16:43
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書目名稱Compression Schemes for Mining Large Datasets影響因子(影響力)學(xué)科排名
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書目名稱Compression Schemes for Mining Large Datasets被引頻次
書目名稱Compression Schemes for Mining Large Datasets被引頻次學(xué)科排名
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書目名稱Compression Schemes for Mining Large Datasets年度引用學(xué)科排名
書目名稱Compression Schemes for Mining Large Datasets讀者反饋
書目名稱Compression Schemes for Mining Large Datasets讀者反饋學(xué)科排名
作者: 紋章 時間: 2025-3-21 22:46 作者: Enzyme 時間: 2025-3-22 01:10
,1919–1923 “Independence or Death!”,vide a better classification accuracy than the original dataset. In this direction, we implement the proposed scheme on two large datasets, one with binary-valued features and the other with float-point-valued features. At the end of the chapter, we provide bibliographic notes and a list of referenc作者: 荒唐 時間: 2025-3-22 06:10 作者: 不怕任性 時間: 2025-3-22 09:15
Product Mix and Diversification,ow the divide-and-conquer approach of multiagent systems improves handling huge datasets. We propose four multiagent systems that can help generating abstraction with big data. We provide suggested reading and bibliographic notes. A list of references is provided in the end.作者: 潔凈 時間: 2025-3-22 15:58
2191-6586 e in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.978-1-4471-7055-6978-1-4471-5607-9Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: 潔凈 時間: 2025-3-22 18:20
Data Mining Paradigms,n intermediate representation. The discussion on classification includes topics such as incremental classification and classification based on intermediate abstraction. We further discuss frequent-itemset mining with two directions such as divide-and-conquer itemset mining and intermediate abstracti作者: FOVEA 時間: 2025-3-23 00:18
Dimensionality Reduction by Subsequence Pruning,earest neighbors. This results in lossy compression in two levels. Generating compressed testing data forms an interesting scheme too. We demonstrate significant reduction in data and its working on large handwritten digit data. We provide bibliographic notes and references at the end of the chapter作者: FLUSH 時間: 2025-3-23 02:14
Data Compaction Through Simultaneous Selection of Prototypes and Features,vide a better classification accuracy than the original dataset. In this direction, we implement the proposed scheme on two large datasets, one with binary-valued features and the other with float-point-valued features. At the end of the chapter, we provide bibliographic notes and a list of referenc作者: Ornament 時間: 2025-3-23 06:57 作者: 運氣 時間: 2025-3-23 12:21
Big Data Abstraction Through Multiagent Systems,ow the divide-and-conquer approach of multiagent systems improves handling huge datasets. We propose four multiagent systems that can help generating abstraction with big data. We provide suggested reading and bibliographic notes. A list of references is provided in the end.作者: Conducive 時間: 2025-3-23 16:02
Introduction,lid representative subsets of original data and feature sets. All further data mining analysis can be based only on these representative subsets leading to significant reduction in storage space and time. Another important direction is to compress the data by some manner and operate in the compresse作者: declamation 時間: 2025-3-23 19:02
Data Mining Paradigms, data mining. We elaborate some important data mining tasks such as clustering, classification, and association rule mining that are relevant to the content of the book. We discuss popular and representative algorithms of partitional and hierarchical data clustering. In classification, we discuss th作者: precede 時間: 2025-3-23 22:55 作者: 令人發(fā)膩 時間: 2025-3-24 02:33 作者: 大溝 時間: 2025-3-24 09:47 作者: FRAUD 時間: 2025-3-24 13:32 作者: 鑲嵌細工 時間: 2025-3-24 17:15
Optimal Dimensionality Reduction,ucing the features include conventional feature selection and extraction methods, frequent item support-based methods, and optimal feature selection approaches. In earlier chapters, we discussed feature selection based on frequent items. In the present chapter, we combine a nonlossy compression sche作者: 描述 時間: 2025-3-24 22:47
Big Data Abstraction Through Multiagent Systems,tems. Big data is characterized by huge volumes of data that are not easily amenable for generating abstraction; variety of data formats, data frequency, types of data, and their integration; real or near-real time data processing for generating business or scientific value depending on nature of da作者: Bone-Scan 時間: 2025-3-25 01:53 作者: peritonitis 時間: 2025-3-25 04:38 作者: Root494 時間: 2025-3-25 07:41
https://doi.org/10.1007/978-3-319-77926-3arious aspects of compression schemes both in abstract sense and as practical implementation. We provide a brief summary of content of each chapter of the book and discuss its overall organization. We provide literature for further study at the end.作者: 污穢 時間: 2025-3-25 12:07
https://doi.org/10.1057/9780230210660fication happens to be a fitness function. We provide a few application scenarios in data mining. We provide theoretical discussions on the scheme. Bibliographic notes provide a brief discussion on important relevant references. A list of references is provided in the end.作者: SSRIS 時間: 2025-3-25 18:31
https://doi.org/10.1007/978-3-030-39037-2 essence, the approach emphasizes exploitation of domain knowledge in mining large datasets, which in the present case results in significant compaction in the data and multiclass classification. We provide a discussion on relevant literature and a list of references at the end of the chapter.作者: 祖先 時間: 2025-3-25 20:50
Introduction,arious aspects of compression schemes both in abstract sense and as practical implementation. We provide a brief summary of content of each chapter of the book and discuss its overall organization. We provide literature for further study at the end.作者: SPALL 時間: 2025-3-26 00:47
Run-Length-Encoded Compression Scheme,fication happens to be a fitness function. We provide a few application scenarios in data mining. We provide theoretical discussions on the scheme. Bibliographic notes provide a brief discussion on important relevant references. A list of references is provided in the end.作者: Conserve 時間: 2025-3-26 06:51 作者: IRK 時間: 2025-3-26 09:54 作者: ODIUM 時間: 2025-3-26 12:54 作者: 健談的人 時間: 2025-3-26 17:46 作者: 催眠 時間: 2025-3-26 20:56 作者: 摘要記錄 時間: 2025-3-27 03:36
Trans-European Telecommunication Networks, features in the given representation of patterns, we would still be able to generate an abstraction that is as accurate in classification as the one with original feature set. In this chapter, we propose a lossy compression scheme. We demonstrate its efficiency and accuracy on practical datasets. T作者: ornithology 時間: 2025-3-27 06:42 作者: 馬具 時間: 2025-3-27 13:08 作者: 偶像 時間: 2025-3-27 17:21
Product Mix and Diversification,ucing the features include conventional feature selection and extraction methods, frequent item support-based methods, and optimal feature selection approaches. In earlier chapters, we discussed feature selection based on frequent items. In the present chapter, we combine a nonlossy compression sche作者: Influx 時間: 2025-3-27 19:29
Product Mix and Diversification,tems. Big data is characterized by huge volumes of data that are not easily amenable for generating abstraction; variety of data formats, data frequency, types of data, and their integration; real or near-real time data processing for generating business or scientific value depending on nature of da作者: 使人煩燥 時間: 2025-3-27 22:47
Compression Schemes for Mining Large Datasets978-1-4471-5607-9Series ISSN 2191-6586 Series E-ISSN 2191-6594 作者: ovation 時間: 2025-3-28 04:49 作者: coalition 時間: 2025-3-28 08:56
978-1-4471-7055-6Springer-Verlag London 2013作者: overweight 時間: 2025-3-28 14:28
T. Ravindra Babu,M. Narasimha Murty,S.V. SubrahmanExamines all aspects of data abstraction generation using a least number of database scans.Discusses compressing data through novel lossy and non-lossy schemes.Proposes schemes for carrying out cluste作者: ILEUM 時間: 2025-3-28 15:31
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/c/image/231990.jpg作者: 微粒 時間: 2025-3-28 22:17
Modelling Resources open course to study rational modelling, login repository database to find applicable models, and visit research group website to learn from the modellers. We hope this “3M rule” will be useful to guide the readers to find critical resources. Also, some software used for modelling and simulation on physiology are briefly described.作者: 束縛 時間: 2025-3-28 23:53
f.Das Toolset ist eine umfassende Sammlung des relevanten Designs für Six Sigma.+Lean. Werkzeuge, die für die erfolgreiche Umsetzung von Innovationen notwendig sind. Alle Werkzeuge sind in klarer und übersichtlicher Form abgebildet. Die Chronologie der aufgeführten Werkzeuge entspricht strikt dem Vo
