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 many years. due to its strength energy for fixing advanced difficulties, information mining has been effectively utilized to different parts comparable to enterprise, engineering, social media, and organic technological know-how. lots 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 ailment. extra, the knowledge features of the issues have additionally grown from static to dynamic and spatiotemporal, entire to incomplete, and centralized to allotted, and develop of their scope and measurement (this is named big data). The powerful integration of massive information for decision-making additionally calls for privateness protection.
The contributions to this monograph summarize the advances of knowledge mining within the respective fields. This quantity includes 9 chapters that tackle matters starting from mining info 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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Additional info for Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities
Zhang and Liu (2011a) formulated the problem based on a bipartite graph and proposed an iterative algorithm to solve the problem. The algorithm was based on the following observation: Observation: The sentiment or opinion expressed in a sentence about resource usage is often determined by the flowing triple, (verb, quantifier, noun_term), where noun_term is a noun or a noun phrase representing a resource. The proposed method used such triples to help identify resources in a domain corpus. , water) based on the bipartite graph.
Opinion observer: analyzing and comparing opinions on the web. : Partially supervised text classification. : Opinion target extraction using word-based translation model. : A review selection approach for accurate feature rating estimation. In: Proceedings of International Conference on Computational Linguistics, COLING 2010 (2010) 38 L. Zhang and B. : Exploiting structured ontology to organize scattered online opinions. : Rated aspect summarization of short comments. : Opinion target extraction in Chinese news comments.
In summary, topic modeling is a powerful and flexible modeling tool. It is also very nice conceptually and mathematically. However, it is only able to find some general/rough aspects, and has difficulty in finding fine-grained or precise aspects. We think it is too statistics centric and come with its limitations. It could be fruitful if we can shift more toward natural language and knowledge centric for a more balanced approach. 4 Miscellaneous Methods Yi et al. (2003) proposed a method for aspect extraction based on the likelihoodratio test.