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

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

The box of information mining has made major and far-reaching advances over the last 3 a long time. due to its capability energy for fixing complicated difficulties, facts mining has been effectively utilized to different parts corresponding to company, engineering, social media, and organic technology. lots of those purposes look for styles in advanced structural details. In biomedicine for instance, modeling complicated organic platforms calls for linking wisdom throughout many degrees of technology, from genes to disorder. extra, the information features of the issues have additionally grown from static to dynamic and spatiotemporal, whole to incomplete, and centralized to allotted, and develop of their scope and dimension (this is named big data). The potent integration of huge info for decision-making additionally calls for privateness protection.

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

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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. : Applied Statistics. : Opinion mining and sentiment analysis. : sentiment classification using machine learning techniques. : Web-Scale distributional similarity and entity set expansion.

6 Sentiment polarity of statements involving resources One such type of expressions involves resources, which occur frequently in many application domains. For example, money is a resource in probably every domain (“this phone costs a lot of money”), gas is a resource in the car domain, and ink is a resource in the printer domain. If a device consumes a large quantity of resource, it is undesirable (negative). If a device consumes little resource, it is desirable (positive). For example, the sentences, “This laptop needs a lot of battery power” and “This car eats a lot of gas” imply negative sentiments on the laptop and the car.

2009−02−05 07:01 (601, 254) 2009−02−05 09:14 (811, 60) 2009−02−05 10:58 (810, 55) 2009−02−05 14:29 (820, 100) ... 2009−06−12 09:56 (110, 98) 2009−06−12 11:20 (101, 65) 2009−06−12 20:08 (20, 97) 2009−06−12 22:19 (15, 100) ... Hidden periodic behaviors Periodic Behavior #1 (Period: day; Time span: Sept. ) 8:00−18:00 in the company 20:00−7:30 in the apartment Periodic Behavior #3 (Period: week; Time span: Sept. − May) 13:00−15:00 Mon. and Wed. in the classroom 14:00−16:00 Tues. and Thurs. in the gym Fig.

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