By Lei Zhang, Bing Liu (auth.), Wesley W. Chu (eds.)
The box of information mining has made major and far-reaching advances during the last 3 a long time. due to its power strength for fixing advanced difficulties, facts mining has been effectively utilized to assorted components corresponding to enterprise, engineering, social media, and organic technology. lots of those purposes look for styles in advanced structural info. In biomedicine for instance, modeling advanced organic platforms calls for linking wisdom throughout many degrees of technological know-how, from genes to disorder. extra, the knowledge features of the issues have additionally grown from static to dynamic and spatiotemporal, entire to incomplete, and centralized to dispensed, and develop of their scope and dimension (this is called big data). The potent integration of massive info for decision-making additionally calls for privateness maintenance.
The contributions to this monograph summarize the advances of information mining within the respective fields. This quantity comprises 9 chapters that tackle 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 relief through binary matrix factorization.
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Extra info for Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities
In their case, they only extracted aspects from Pros and Cons of the reviews. Li et al. (2012b) formulated aspect extraction as a shallow semantic parsing problem. A parse tree is built for each sentence and structured syntactic information within the tree is used to identify aspects. 2 Aspect Grouping and Hierarchy It is common that people use different words and expressions to describe the same aspect. For example, photo and picture refer to the same aspect in digital camera reviews. 3) can identify and group aspects to some extent, the results are not fine-grained because such models are based on word co-occurrences rather than word semantic meanings.
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).
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. As mentioned before, mj is the mean of feature j of all possible entities and k is a scaling factor.