By Lei Zhang, Bing Liu (auth.), Wesley W. Chu (eds.)
The box of knowledge mining has made major and far-reaching advances during the last 3 many years. as a result of its power strength for fixing advanced difficulties, info mining has been effectively utilized to varied components comparable to enterprise, engineering, social media, and organic technological know-how. a lot of those functions look for styles in complicated structural details. In biomedicine for instance, modeling advanced organic platforms calls for linking wisdom throughout many degrees of technology, from genes to affliction. extra, the information features of the issues have additionally grown from static to dynamic and spatiotemporal, whole to incomplete, and centralized to disbursed, and develop of their scope and dimension (this is named big data). The powerful integration of massive 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 comprises 9 chapters that handle 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 relief through binary matrix factorization.
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Extra resources for Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities
Even worse, due to the limitations of positioning technology or its various kinds of deployments, real movement data is often highly incomplete and sparse. In this chapter, we discuss existing techniques to mine periodic behaviors from spatiotemporal data, with a focus on tackling the aforementioned difficulties risen in real applications. In particular, we first review the traditional time-series method for periodicity detection. Then, a novel method specifically designed to mine periodic behaviors in spatiotemporal data, Periodica, is introduced.
A dictionary-based method was proposed, which tries to identify attribute nouns from the dictionary gloss of the adjective. , synonyms, antonyms, hyponym and hypernym) for classification. , 2011b). 5 Identifying Aspects That Imply Opinions Zhang and Liu (2011a) found that in some domains nouns and noun phrases that indicate product aspects may also imply opinions. In many such cases, these nouns are not subjective but objective. Their involved sentences are also objective sentences but imply positive or negative opinions.
They believe there are two main reasons. First, since Bayesian Sets uses binary features, multiple occurrences of an entity in the corpus, which give rich contextual information, is not fully exploited. Second, since the number of seeds is very small, the learned results from Bayesian Sets can be quite unreliable. They proposed a method to improve Bayesian Sets, which produces much better results. The main improvements are as follows. Raising Feature Weights: From Equation (21), we can see that the score of an entity ei is determined only by its corresponding feature vector and the weight vector w = (w1, w2, …, wj).
Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities by Lei Zhang, Bing Liu (auth.), Wesley W. Chu (eds.)