W. Pedrycz, "Fuzzy set technology in knowledge discovery," Fuzzy Sets Syst., vol. 98, pp. 279--290, 1998. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Data Mining in Soft Computing Framework: A Survey - Mitra, Pal, Fellow, Mitra (2001) (7 citations) (Correct)
.... and describing them in a concise and meaningful manner [8] Fuzzy models can be said to represent a prudent and user oriented sifting of data, qualitative observations and calibration of commonsense rules in an attempt to establish meaningful and useful relationships between system variables [55]. Despite a growing versatility of knowledge discovery systems, there is an important component of human interaction that is inherent to any process of knowledge representation, manipulation, and processing. Fuzzy sets are inherently inclined toward coping with linguistic domain knowledge and ....
....of real world data in data mining often necessitates simultaneous dealing with different types of variables, viz. categorical symbolic data and numerical data. Nauck [59] has developed a learning algorithm that creates mixed fuzzy rules involving both categorical and numeric attributes. Pedrycz [55] discusses some constructive and fuzzy set driven computational vehicles of knowledge discovery, and establishes the relationship between data mining and fuzzy modeling. The role of fuzzy sets is categorized below based on the different functions of data mining that are modeled. 1) Clustering: ....
W. Pedrycz, "Fuzzy set technology in knowledge discovery," Fuzzy Sets Syst., vol. 98, pp. 279--290, 1998.
Data Mining in Soft Computing Framework: A Survey - Mitra, Pal, Mitra (2001) (7 citations) (Correct)
.... and describing them in a concise and meaningful manner [8] Fuzzy models can be said to represent a prudent and user oriented sifting of data, qualitative observations and calibration of commonsense rules in an attempt to establish meaningful and useful relationships between system variables [55]. Despite a growing versatility of knowledge discovery systems, there is an important component of human interaction that is inherent to any process of knowledge representation, manipulation, and processing. Fuzzy sets are inherently inclined towards coping with linguistic domain knowledge and ....
....of real world data in data mining often necessitates simultaneous dealing with di#erent types of variables, viz. categorical symbolic data and numerical data. Nauck [59] has developed a learning algorithm that creates mixed fuzzy rules involving both categorical and numeric attributes. Pedrycz [55] discusses some constructive and fuzzy setdriven computational vehicles of knowledge discovery, and establishes the relationship between data mining and fuzzy modeling. The role of fuzzy sets is categorized below based on the di#erent functions of data mining that are modeled. A.1 Clustering Data ....
W. Pedrycz, "Fuzzy set technology in knowledge discovery," Fuzzy Sets and Systems, vol. 98, pp. 279--290, 1998.
Automated Perceptions in Data Mining - Last, Kandel (1999) (Correct)
....in the amount of available data. The initial illusion was (and, unfortunately, still is for many people) that improved availability of data leads automatically to improved availability of our knowledge about the World. In fact, we still live in a society where data is rich and knowledge is poor [15]. As we show below, the actual way to the ultimate understanding of data is far from being simple and straightforward. 1.1 Crisp Data Mining I: Classical Statistics Most methods of the classical statistics are verification oriented. They are based on the assumption that the data analyst knows a ....
....is no ambiguity. Otherwise, the overlapping of linguistic terms for a given value increases the vagueness of that value, as perceived by humans. The fuzzy decision tree is induced by selecting the attributes with smallest classification ambiguity at each new decision node. According to Pedrycz [15], specification of linguistic terms, representing the database variables, can deal with one of the central problems of knowledge discovery identifying the most interesting patterns. The notion of interestingness (including such aspects as novelty, usefulness, simplicity, generality, etc. is ....
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W. Pedrycz, Fuzzy Set Technology in Knowledge Discovery, Fuzzy Sets and Systems, vol. 98, no. 3, pp. 279-290, 1998.
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