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Get Data Mining and Knowledge Discovery for Big Data: PDF

By Lei Zhang, Bing Liu (auth.), Wesley W. Chu (eds.)

ISBN-10: 3642408362

ISBN-13: 9783642408366

ISBN-10: 3642408370

ISBN-13: 9783642408373

The box of knowledge mining has made major and far-reaching advances during the last 3 a long time. as a result of its strength energy for fixing advanced difficulties, information mining has been effectively utilized to various components resembling company, engineering, social media, and organic technological know-how. a lot of those functions look for styles in complicated structural info. In biomedicine for instance, modeling complicated organic platforms calls for linking wisdom throughout many degrees of technological know-how, from genes to illness. additional, the knowledge features of the issues have additionally grown from static to dynamic and spatiotemporal, entire to incomplete, and centralized to allotted, and develop of their scope and measurement (this is named big data). The powerful integration of huge info for decision-making additionally calls for privateness renovation.

The contributions to this monograph summarize the advances of information mining within the respective fields. This quantity includes 9 chapters that deal with topics starting from mining facts from opinion, spatiotemporal databases, discriminative subgraph styles, course wisdom discovery, social media, and privateness concerns to the topic of computation aid through binary matrix factorization.

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Additional info for Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities

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Grouping product features using semi-supervised learning with soft-constraints. : Extracting and ranking product features in opinion documents. : Identifying noun product features that imply opinions. : Extracting resource terms for sentiment analysis. : Entity set expansion in opinion documents. : Jointly modeling aspects and opinions with a MaxEntLDA hybrid. : Multi-aspect opinion polling from textual reviews. : Movie review mining and summarization. In: Proceedings of ACM International Conference on Information and Knowledge Management, CIKM 2006 (2006) Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data Zhenhui Li and Jiawei Han Abstract.

Then, an undirected weighted graph is constructed. Each vertex represents an aspect. Each edge weight is proportional to the probability p between two vertices. Finally, approximate graph partitioning methods are employed to group product aspects. Closely related to aspect grouping, aspect hierarchy is to present product aspects as a tree or hierarchy. The root of the tree is the name of the entity. Each non-root node is a component or sub-component of the entity. Each link is a partof relation.

Zhang and B. : Exploiting structured ontology to organize scattered online opinions. : Rated aspect summarization of short comments. : Opinion target extraction in Chinese news comments. : Structuring e-commerce inventory. : Aspect extraction through semi-supervised modeling. : Topic sentiment mixture: modeling facets and opinions in weblogs. : Opinion digger: an unsupervised opinion miner from unstructured product reviews. : ILDA: interdependent LDA model for learning latent aspects and their ratings from online product reviews.

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Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities by Lei Zhang, Bing Liu (auth.), Wesley W. Chu (eds.)


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