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

Prostatism

https://doi.org/10.1007/978-3-642-51833-1 research, we extend the text mining system for the call summary records and construct a conversation mining system for the business-oriented conversation at the contact center. To acquire useful business insights from the conversation data through the text mining system, it is critical to identify

Interlocking

Commentar zur Pharmacopoea Germanica There is relatively little effort in identifying scams in Twitter. In this chapter, we propose a semi-supervised Twitter scam detector based on a small labeled data. The scam detector combines self-learning and clustering analysis. A suffix tree data structure is used. Model building based on Akaik

Cryptic

Commentar zur Pharmacopoea Germanicais mainly limited to analyzing a single data source. In this chapter, we propose a novel joint matrix factorization framework which can jointly analyze multiple data sources by exploiting their shared and individual structures. The proposed framework is flexible to handle any arbitrary sharing confi

Forage飼料

Commentar zur Pharmacopoea Germanica networks by filtering information and offering useful recommendations to customers. Collaborative Filtering (CF) is believed to be a suitable underlying technique for recommendation systems based on social networks, and social networks provide the needed collaborative social environment. CF and its

不連貫

Commentar zur Pharmacopoea Germanicaer classes. Additionally, in many real-life problem domains, data with an imbalanced class distribution contains ambiguous regions in the data space where the prior probability of two or more classes are approximately equal. This problem, known as overlapping classes, thus makes it difficult for the

faction

https://doi.org/10.1007/978-3-642-51833-1ituation is an important technical challenge. In this chapter, we focus on . technologies, including the tasks of outlier detection and change-point detection. In particular, we focus on how to handle the heterogeneous and dynamic natures that are common features of the data in service businesses. W

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Exuberance

先行

https://doi.org/10.1007/978-3-642-45252-9Data Mining; Domain Knowledge; Large Database; Sensor Network; Social Media; Strategic Use of Data

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