A survey of Knowledge Discovery and Data Mining process models

By Lukasz Kurgan, Petr Musilek. Knowledge engineering review (Print), 2006

428 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/72b239d3cd83572e23c5cecdd40af99e94f43550

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Andrew J. Clark
2. Hussein A. Abbass
3. Andreas Holzinger
4. Gonzalo Mariscal
5. Covadonga Fernández
6. Ó. Marbán
7. George Leu
8. Jonathan J. Davis

In Control and Systems Engineering

1. Andrew J. Clark
2. Andreas Holzinger
3. Gonzalo Mariscal
4. Covadonga Fernández
5. Ó. Marbán
6. Jonathan J. Davis

In Information Systems

1. Andrew J. Clark
2. Hussein A. Abbass
3. Andreas Holzinger
4. Gonzalo Mariscal
5. Covadonga Fernández
6. Solon Barocas
7. Ó. Marbán
8. George Leu

Citations in the corpus

• Problem Formulation and Fairness (2019, background): “As Kurgan and Musilek [26] explain, the push “to formally structure [data mining] as a process results from an observation of problems associated with a blind application of [data mining] methods to input data.”
• Ontology-Guided Principal Component Analysis: Reaching the Limits of the Doctor-in-the-Loop (2016, background): “In the commonly known definitions of this process (see [19] for a good overview) the domain-expert is seen in a supervising, consulting and customer role.”
• Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “Various formal process models have been proposed for knowledge discovery and data mining (KDDM), as reviewed by Kurgan and Musilek (2006).”
• A survey of data mining and knowledge discovery process models and methodologies (2010, methodology): “In general, data mining is used by many researchers as a synonym of the KDD process (Cabena et al ., 1997; Chapman et al ., 2000; Piatetsky-Shapiro, 2000; Kurgan & Musilek, 2006).”
• Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research (2016, background): “In the commonly known definitions (see [3] for a good overview), the domain-expert is seen in a supervising, consulting, and a customer role: a person who is outside the process and assists in crucial aspects with domain knowledge and receives the results.”
• A multi-disciplinary review of knowledge acquisition methods: From human to autonomous eliciting agents (2016, background): “Also, from a KD perspective Kurgan and Musilek [124] identified and reviewed the existing models for the KA process, where the models assumed a number of discrete activities or stages.”