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Titlebook: Data Warehousing and Knowledge Discovery; 12th International C Torben Bach Pedersen,Mukesh K. Mohania,A Min Tjoa Conference proceedings 201

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書目名稱Data Warehousing and Knowledge Discovery
副標題12th International C
編輯Torben Bach Pedersen,Mukesh K. Mohania,A Min Tjoa
視頻videohttp://file.papertrans.cn/264/263200/263200.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Data Warehousing and Knowledge Discovery; 12th International C Torben Bach Pedersen,Mukesh K. Mohania,A Min Tjoa Conference proceedings 201
描述Data warehousing and knowledge discovery has been widely accepted as a key te- nology for enterprises and organizations to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision-making process, the data to be considered become more and more complex in both structure and semantics. New developments such as cloud computing add to the challenges with massive scaling, a new computing infrastructure, and new types of data. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data forms the litmus test for research in the area. In the last decade, the International Conference on Data Warehousing and Kno- edge Discovery (DaWaK) has become one of the most important international sci- tific events bringing together researchers, developers, and practitioners to discuss the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. th This year’s conference, the 12 International Conference on Data Warehousing an
出版日期Conference proceedings 2010
關(guān)鍵詞Business Intelligence; algorithms; business process intelligence; clustering; data cleansing; data mining
版次1
doihttps://doi.org/10.1007/978-3-642-15105-7
isbn_softcover978-3-642-15104-0
isbn_ebook978-3-642-15105-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Heidelberg 2010
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Computer Analysis of Images and Patternssearches for core sets, i.e. groups of nodes that are densely connected to each other by efficiently exploring the two-dimensional core parameter space, and successively expands the identified cores by maximizing a local subgraph quality measure. We evaluate this scheme on three real-world tag netwo
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Computer Analysis of Images and Patternsg. These are also issues with respect to other forms of frequent pattern mining but are exacerbated in the context of frequent sub-graph mining. To reduced the search space, and address these twin overheads, a weighted approach to sub-graph mining is proposed. However, a significant issue in weighte
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Computer Analysis of Images and Patterns., contrary to traditional design approaches that assume that cluster nodes are instead ., and fragmentation and allocation phases are performed in a ., contrary to traditional design approaches that instead perform these phases in an .. Also, a naive replication algorithm that takes into account th
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