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Titlebook: Data Mining for Service; Katsutoshi Yada Book 2014 Springer-Verlag Berlin Heidelberg 2014 Data Mining.Domain Knowledge.Large Database.Sens

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發(fā)表于 2025-3-21 19:59:42 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Mining for Service
編輯Katsutoshi Yada
視頻videohttp://file.papertrans.cn/263/262952/262952.mp4
概述Presents a new way for strategic.Use of Large-Scale Data Sets in Business.Demonstrates how Data Mining can be used to Revitalize your Business.Written by leading experts in the field.Includes suppleme
叢書名稱Studies in Big Data
圖書封面Titlebook: Data Mining for Service;  Katsutoshi Yada Book 2014 Springer-Verlag Berlin Heidelberg 2014 Data Mining.Domain Knowledge.Large Database.Sens
描述.Virtually all nontrivial and modern service related problems and systems involve data volumes and types that clearly fall into what is presently meant as "big data", that is, are huge, heterogeneous, complex, distributed, etc..Data mining is a series of processes which include collecting and accumulating data, modeling phenomena, and discovering new information, and it is one of the most important steps to scientific analysis of the processes of services..Data mining application in services requires a thorough understanding of the characteristics of each service and knowledge of the compatibility of data mining technology within each particular service, rather than knowledge only in calculation speed and prediction accuracy. Varied examples of services provided in this book will help readers understand the relation between services and data mining technology. This book is intended to stimulate interest among researchers and practitioners in the relation between data mining technology and its application to other fields..
出版日期Book 2014
關(guān)鍵詞Data Mining; Domain Knowledge; Large Database; Sensor Network; Social Media; Strategic Use of Data
版次1
doihttps://doi.org/10.1007/978-3-642-45252-9
isbn_softcover978-3-662-50743-8
isbn_ebook978-3-642-45252-9Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightSpringer-Verlag Berlin Heidelberg 2014
The information of publication is updating

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Paul van de Laar,Arie van der Schoorl evaluation on two benchmark corpora. These experiments indicate that our algorithm can deliver a substantial reduction in the number of features, from 8,742 to 500 and from 47,236 to 392 features, while preserving or even improving the retrieval performance.
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Commentar zur Pharmacopoea Germanicar different sources. The system we proposed here is the Multi-Collaborative Filtering Trust Network Recommendation System, which combined multiple online sources, measured trust, temporal relation and similarity factors.
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Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Termsl evaluation on two benchmark corpora. These experiments indicate that our algorithm can deliver a substantial reduction in the number of features, from 8,742 to 500 and from 47,236 to 392 features, while preserving or even improving the retrieval performance.
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Scam Detection in Twittere and Bayes Information Criteria is investigated and combined with the classification step. Our experiments show that 87?% accuracy is achievable with only 9 labeled samples and 4000 unlabeled samples, among other results.
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Change Detection from Heterogeneous Data Sourcese describe an approach of singular spectrum transformation for change-point detection for heterogeneous data. We also introduce a novel technique of proximity-based outlier detection to handle the dynamic nature of the data. Using real-world sensor data, we demonstrate the utility of the proposed methods.
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