Data Mining: Foundations and Intelligent Paradigms: Volume - download pdf or read online

February 2, 2018 | Data Mining | By admin | 0 Comments

By Dawn E. Holmes, Lakhmi C Jain

ISBN-10: 3642231500

ISBN-13: 9783642231506

There are many precious books to be had on info mining idea and functions. although, in compiling a quantity titled “DATA MINING: Foundations and clever Paradigms: quantity three: clinical, overall healthiness, Social, organic and different purposes” we want to introduce a number of the most up-to-date advancements to a wide viewers of either experts and non-specialists during this field.

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Extra resources for Data Mining: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, Social, Biological and other Applications

Example text

In addition, based on the experimental results, we are inclined to reject our hypothesis. 5 Conclusion In this chapter, we proposed BioKeySpotter, a novel unsupervised keyphrase extraction technique for biomedical full-text collections. We utilized lexical syntactic features to extract candidate keyphrases. We categorized candidate keyphrases into six different groups and assign different weight to a keyphrase according to the category where each keyphrase belongs to. We chose pair-wise mutual information to calculate weight of each keyphrase.

6278, pp. 418–425. : Temporal Association Rules for Gene Regulatory Networks. : Time-annotated Sequences for Medical Data Mining. In: 7th IEEE International Conference on Data Mining, pp. : Temporal Data Mining with Temporal Constraints. In: Proceedings of the 11th Conference on Artificial Intelligence in Medicine, Amsterdam, The Netherlands, pp. : Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions. : Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors.

Data Mining: Found. & Intell. Paradigms, ISRL 25, pp. 19–27. com © Springer-Verlag Berlin Heidelberg 2011 20 M. Song and P. Tanapaisankit The major contribution of our study is three-fold: 1) BioKeySpotter is an unsupervised learning technique, which can be easily adapted to other domains, 2) the performance of BioKeySpotter is superior to, or at least equivalent to, classification-based keyphrase extraction algorithms, and 3) BioKeySpotter addresses and resolves the problem with extracting keyphrases from full-text.

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Data Mining: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, Social, Biological and other Applications by Dawn E. Holmes, Lakhmi C Jain


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