Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

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Modern Applied Statistics With S

International study of the prevalence and outcomes of infection in intensive care units

Brewers' spent grain : generation, characteristics and potential applications

In the last years there has been a huge growth and consolidation of the Data Mining field. Some efforts are being done that seek the establishment of standards in the area. Included on these efforts there can be enumerated SEMMA and CRISP-DM. Both grow as industrial standards and define a set of sequential steps that pretends to guide the implementation of data mining applications. The question of the existence of substantial differences between them and the traditional KDD process arose. In this paper, is pretended to establish a parallel between these and the KDD process as well as an understanding of the similarities between them.

/pdf/kdd-semma-and-crisp-dm-a-parallel-overview-4iwpxofdkf.pdf

Kdd, semma and crisp-dm: a parallel overview

Variability of brewer's spent grain within a brewery

http://www3.dsi.uminho.pt/pcortez/sofa3.pdf

Rating organ failure via adverse events using data mining in the intensive care unit

https://repositorium.sdum.uminho.pt/bitstream/1822/5412/1/death4.pdf

Manuel Filipe Santos

Papers

Kdd, semma and crisp-dm: a parallel overview

Variability of brewer's spent grain within a brewery

Rating organ failure via adverse events using data mining in the intensive care unit

Mortality assessment in intensive care units via adverse events using artificial neural networks

Pentoses and Hydroxycinnamic Acids in Brewer's Spent Grain