Assisting Data Mining through Automated Planning

Fernández, Fernando; Borrajo, Daniel; Fernández, Susana; Manzano, David

doi:10.1007/978-3-642-03070-3_57

Cited by 1 publication

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…approaches (Michalski & Kaufman 1998, Fernández, et al 2009, Kietz, et al 2009, Diamantini, et al 2009, Bernstein, et al 2005, Zakova, et al 2008.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the DM process is sometimes seen as an expert process where DM engineers transform original data, execute different mining operators, evaluate the obtained models, and repeat this process until they are satisfied. The complexity of this combinatorial process suggests using automated approaches (Michalski & Kaufman, 1998; Bernstein et al ., 2005; Zakova et al ., 2008; Diamantini et al ., 2009; Fernández et al ., 2009; Kietz et al ., 2009).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using automated planning for improving data mining processes

Fernández

Rosa

Fernández

et al. 2013

The Knowledge Engineering Review

Self Cite

View full text Add to dashboard Cite

This paper presents a distributed architecture for automating data mining processes using standard languages. Data mining is a difficult task that relies on an exploratory and analytic process of processing large quantities of data in order to discover meaningful patterns. The increasing heterogeneity and complexity of available data requires some expert knowledge on how to combine the multiple and alternative data mining tasks to process the data. Here, we describe data-mining tasks in terms of Automated Planning, which allows us to automate the data-mining knowledge flow construction. The work is based on the use of standards that have been defined in both data mining and automated-planning communities. Thus, we use PMML (Predictive Model Markup Language) to describe data mining tasks. From the PMML, a problem description in PDDL (Planning Domain Definition Language) can be generated, so any current planning system can be used to generate a plan. This plan is, again, translated to a data-mining workflow description, KFML format (Knowledge Flow file for the WEKA tool), so the plan or data-mining workflow can be executed in WEKA (Waikato Environment for Knowledge Analysis).

show abstract

“…approaches (Michalski & Kaufman 1998, Fernández, et al 2009, Kietz, et al 2009, Diamantini, et al 2009, Bernstein, et al 2005, Zakova, et al 2008.…”

Section: Introductionmentioning

confidence: 99%