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

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

The box of knowledge mining has made major and far-reaching advances over the last 3 many years. as a result of its capability energy for fixing advanced difficulties, information mining has been effectively utilized to assorted parts corresponding to enterprise, engineering, social media, and organic technology. a lot of those functions look for styles in advanced structural info. In biomedicine for instance, modeling advanced organic platforms calls for linking wisdom throughout many degrees of technology, from genes to illness. additional, the knowledge 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 measurement (this is named big data). The potent integration of huge facts 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 tackle matters starting from mining info from opinion, spatiotemporal databases, discriminative subgraph styles, direction wisdom discovery, social media, and privateness matters to the topic of computation aid through binary matrix factorization.

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Extra resources for Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities

Example text

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. Each node is associated with a set of product aspects. Yu et al. (2011b) proposed a method to create aspect hierarchy.

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). Equation (22) shows a value of the weight vector w. They rewrite Equation (22) as follows, N N ij   α~ j βj i =1 − log = log(1 + i =1 ) − log(1 + w j = log ) αj βj km j k (1 − m j ) qij ~ N− q (23) In Equation (23), N is the number of items in the seed set.

Moghaddam and Ester (2011) proposed a model called ILDA, which is based on LDA and jointly models latent aspects and rating. ILDA can be viewed as a generative process that first generates an aspect and subsequently generates its rating. In particular, for generating each opinion phrase, ILDA first generates an aspect am from an LDA model. Then it generates a rating rm conditioned on the sampled aspect am. Finally, a head term tm and a sentiment sm are drawn conditioned on am and rm, respectively.

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