Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1991). Knowledge discovery in databases. In G. Piatetsky-Shapiro & W. J. Frawley (Eds.), Knowledge Discovery in databases: an overview (pp. 1--27). Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
From Unsupervised Learning to Data Mining: Linking Cognition and .. - Talavera (Correct)
....set. Particularly, they claim that many ML algorithms assume that a small, well structured, errorfree dataset from which learning takes place exists. Using a database as a training set for ML systems may cause several problems as databases contain data generated for purposes other than learning (Frawley et al. 1991, Holsheimer Siebes, 1994) The first problem is the size of databases. A data mining system is expected to deal with large databases, containing large data volumes. In addition, in many databases, objects are described by a large number of fields, that is, they have a high dimensionality. A ....
Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1991). Knowledge discovery in databases. In G. Piatetsky-Shapiro & W. J. Frawley (Eds.), Knowledge Discovery in databases: an overview (pp. 1--27).
Case-Based Classification Using Similarity-Based Retrieval - Jurisica, Glasgow (1996) (Correct)
....(see Section 4.2) three categories are used: motor and screw form the first category, pgain and vgain comprise the second category, and the last category is the class rise time. The membership is defined either using domain knowledge (if available) or using a knowledge discovery algorithm [11, 19, 24]. This grouping allows for ascribing different constraints to different groups of attributes, and the process of retrieving relevant cases can be described as a constraint satisfaction process [39, 13, 27] Categories allow for improved system performance, as will be shown later. An explicitly ....
J. Frawley and G. Piatetsky-Shapiro. Knowledge Discovery in Databases. AAAI Press, 1991.
Improving Performance Of Case-Based Classification Using.. - Jurisica, Glasgow (1997) (Correct)
....are grouped into three categories: motor and screw form the first category; pgain and vgain comprise the second category; and the last category is the class rise time. Attribute membership in a particular category is defined either using domain knowledge or using a knowledge discovery algorithm [12, 23, 28]. This grouping allows for ascribing different constraints to different groups of attributes, and the process of retrieving relevant cases can be described as a constraint satisfaction process [45, 14, 31] Categories allow for improved system performance, as will be shown later. An explicitly ....
J. Frawley and G. Piatetsky-Shapiro. Knowledge Discovery in Databases. AAAI Press, 1991.
An Empirical Study of Domain Knowledge and Its Benefits to.. - Djoko, Cook, Holder (1999) (3 citations) (Correct)
....With the increasing amount and complexity of today s data, there is an urgent need to accelerate discovery of information in databases. In response to this need, numerous approaches have been developed for discovering concepts in databases using a linear, attribute value representation [1] 2] [3], 4] 5] These approaches address issues of data relevance, missing data, noise, and utilization of domain knowledge. However, much of the data that is collected is structural in nature, or is composed of parts and relations between the parts. Hence, there exists a need for methods to analyze ....
W. J. Frawley, G. Piatetsky-Shapiro, and editors C. J. Matheus, Knowledge Discovery in Databases, AAAI Press / The MIT Press, 1991.
A Genetic Algorithm-Based Approach to Data Mining - Flockhart, Radcliffe (1996) (6 citations) (Correct)
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Frawley, editors, Knowledge Discovery in Databases, pages 159--176. MIT Press. Patrick D. Surry and Nicholas J. Radcliffe, 1994a. The Reproductive Plan Language RPL2. Edinburgh Parallel Computing Centre.
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