Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey

The mass of data available on the Internet is rapidly increasing; the complexity of this data is discussed at the level of the multiplicity of information sources, formats, modals, and versions. Facing the complexity of biological data, such as the DNA sequences, protein sequences, and protein structures, the biologist cannot simply use the traditional techniques to analyze this type of data. The knowledge extraction process with data mining methods for the analysis and processing of biological complex data is considered a real scientific challenge in the search for systematically potential relationships without prior knowledge of the nature of these relationships. In this chapter, the authors discuss the Knowledge Discovery in Databases process (KDD) from the Biological Data. They specifically present a state of the art of the best known and most effective methods of data mining for analysis of the biological data and problems of bioinformatics related to data mining.

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Thermodynamic design space data-mining and multi-objective optimization of SCO2 Brayton cycles

Energy Conversion and Management ◽

10.1016/j.enconman.2021.114844 ◽

2021 ◽

Vol 249 ◽

pp. 114844

Author(s):

Tao Zhou ◽

Zhengxian Liu ◽

Xiaojian Li ◽

Ming Zhao ◽

Yijia Zhao

Keyword(s):

Data Mining ◽

Design Space ◽

Multi Objective Optimization ◽

Multi Objective ◽

Space Data

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ON THE ROLE OF INTERPRETABILITY IN FUZZY DATA MINING

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488507004856 ◽

2007 ◽

Vol 15 (05) ◽

pp. 521-537 ◽

Cited By ~ 17

Author(s):

CORRADO MENCAR ◽

GIOVANNA CASTELLANO ◽

ANNA M. FANELLI

Keyword(s):

Data Mining ◽

Fuzzy Logic ◽

Knowledge Discovery ◽

End Users ◽

Careful Consideration ◽

Fuzzy Data ◽

Systematic Methodology ◽

Mining Methods

Data Mining, a central step in the broader overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential — yet rarely tackled — feature that makes resulting patterns accessible by end users. In this paper we argue that the adoption of Fuzzy Logic for Data Mining can improve understandability of derived patterns. Indeed, Fuzzy Logic is able to represent concepts in a “human-centric” way. Hence, Data Mining methods based on Fuzzy Logic may potentially meet the so-called “Comprehensibility Postulate”, which characterizes the blurry notion of understandability. However, the mere adoption of Fuzzy Logic for Data Mining is not enough to achieve understandability. This paper describes and comments a number of issues that need to be addressed to provide for understandable patterns. A careful consideration of all such issues may end up in a systematic methodology to discover comprehensible knowledge from data.

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