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Knowledge extraction

About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.


Papers
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Journal ArticleDOI
TL;DR: This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.
Abstract: Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.

270 citations

Journal ArticleDOI
TL;DR: A rule induction method is introduced, which extracts not only classification rules but also other medical knowledge needed for diagnosis from clinical cases, and is evaluated on three clinical databases.

270 citations

Proceedings ArticleDOI
12 Oct 1997
TL;DR: The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, derivation of causal relationships between inputs and outputs, and analysis of the hidden neuron behavior in classification ANNs.
Abstract: The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, derivation of causal relationships between inputs and outputs, and analysis of the hidden neuron behavior in classification ANNs. An example of the application of these techniques is given of the faulty LED display benchmark. References of the application of these techniques are given of diverse large scale ANN models of industrial processes.

270 citations

Journal ArticleDOI
TL;DR: The aim is to list some of the pressing research challenges, and outline opportunities for contributions by the optimization research communities, and include formulations of the basic categories of data mining methods as optimization problems.
Abstract: This article is intended to serve as an overview of a rapidly emerging research and applications area. In addition to providing a general overview, motivating the importance of data mining problems within the area of knowledge discovery in databases, our aim is to list some of the pressing research challenges, and outline opportunities for contributions by the optimization research communities. Towards these goals, we include formulations of the basic categories of data mining methods as optimization problems. We also provide examples of successful mathematical programming approaches to some data mining problems.

269 citations

Patent
11 Mar 2002
TL;DR: In this paper, the authors proposed a method for constructing segmentation-based predictive models, such as decision-tree classifiers, where data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment.
Abstract: The present invention generally relates to computer databases and, more particularly, to data mining and knowledge discovery. The invention specifically relates to a method for constructing segmentation-based predictive models, such as decision-tree classifiers, wherein data records are partitioned into a plurality of segments and separate predictive models are constructed for each segment. The present invention contemplates a computerized method for automatically building segmentation-based predictive models that substantially improves upon the modeling capabilities of decision trees and related technologies, and that automatically produces models that are competitive with, if not better than, those produced by data analysts and applied statisticians using traditional, labor-intensive statistical techniques. The invention achieves these properties by performing segmentation and multivariate statistical modeling within each segment simultaneously. Segments are constructed so as to maximize the accuracies of the predictive models within each segment. Simultaneously, the multivariate statistical models within each segment are refined so as to maximize their respective predictive accuracies.

269 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023120
2022285
2021506
2020660
2019740
2018683