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Titlebook: Big Data Analytics and Knowledge Discovery; 17th International C Sanjay Madria,Takahiro Hara Conference proceedings 2015 Springer Internati

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期刊全稱Big Data Analytics and Knowledge Discovery
期刊簡(jiǎn)稱17th International C
影響因子2023Sanjay Madria,Takahiro Hara
視頻videohttp://file.papertrans.cn/186/185610/185610.mp4
發(fā)行地址Includes supplementary material:
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Big Data Analytics and Knowledge Discovery; 17th International C Sanjay Madria,Takahiro Hara Conference proceedings 2015 Springer Internati
影響因子.This book constitutes the refereed proceedings of the 17th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2015, held in Valencia, Spain, September 2015..The 31 revised full papers presented were carefully reviewed and selected from 90 submissions. The papers are organized in topical sections similarity measure and clustering; data mining; social computing; heterogeneos networks and data; data warehouses; stream processing; applications of big data analysis; and big data..
Pindex Conference proceedings 2015
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978-3-319-22728-3Springer International Publishing Switzerland 2015
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Big Data Analytics and Knowledge Discovery978-3-319-22729-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Introducing Foreign Models for Developmentmilarity measures and traditional clustering algorithms are not reliable in separating clusters from each other. For example, when too many dimensions are considered simultaneously, objects become unique and (dis-)similarity does not provide meaningful information to detect clusters anymore. While t
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Introduction to Using a Computer System,ould find the complete set of patterns and then apply a post-pruning step to it. The size of the complete mining results is typically prohibitively large, despite the fact that only a small percentage of high utility patterns are interesting. Thus it is inefficient to wait for the mining algorithm t
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Composing Functions Using Pipelininguently, they require a significant amount of storage space to capture all existential probability values among the items in the data. To reduce the amount of required storage space, some existing algorithms (e.g., PUF-growth) combine nodes with the same item by storing an upper bound on expected sup
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