{"id":51,"date":"2010-08-30T09:27:13","date_gmt":"2010-08-30T07:27:13","guid":{"rendered":"http:\/\/lwibs01.gm.fh-koeln.de\/blogs\/ciop\/?page_id=51"},"modified":"2025-01-20T09:15:00","modified_gmt":"2025-01-20T08:15:00","slug":"tuned-data-mining","status":"publish","type":"page","link":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/tuned-data-mining\/","title":{"rendered":"TDMR: Tuned Data Mining in R"},"content":{"rendered":"

Tuned Data Mining (TDM) and TDMR\u200b<\/strong><\/span><\/p>\r\n\"logo_data_mining\"<\/a>\r\n

NOTE 03\/2020: TDMR 2.2<\/strong> on CRAN<\/a> is now available !!<\/p>\r\n

The complex, often redundant and noisy data of real-world data mining (DM) applications frequently lead to inferior results when out-of-the-box DM models are applied. Tuning of parameters is essential to achieve high-quality results. We pursue in this project an approach to tune parameters of the preprocessing and the modelling phase conjointly. We propose the new framework TDM (Tuned Data Mining) which facilitates the search for good parameters and the comparison of different tuners by using mostly generic elements which are easily applied to new tasks.<\/p>\r\n

The\u00a0R<\/a>-package\u00a0TDMR<\/a><\/strong>\u00a0(Tuned Data Mining in R<\/strong>) - freely available as open-source software from CRAN<\/a> - is written with the aim to facilitate the setup, training and \"28921_small_christophe-papke_pixelio.de_\"<\/a>evaluation of data mining models. It puts special emphasis on tuning these data mining models as well as simultaneously tuning certain preprocessing options. TDMR is especially designed to work with SPOT<\/a>\u00a0as the preferred tuner, but it offers also the possibility to use other tuners (CMA-ES, LHD, direct-search optimizers) for comparison.<\/p>\r\n

See the user manual\u00a0TDMR-docu.pdf<\/a> (CIOP-Report 02\/2018 [Kone18a], last update March 2020<\/strong>) and the\u00a0TDMR Tutorial<\/a> (CIOP-Report 03\/2018 [Kone18b], last update March 2020<\/strong>) for in-depth information on usage and development of the TDMR package.<\/p>\r\n

TDMR 2.2<\/a>, available on CRAN since March'2020, offers a simplified interface and integration with SPOT 2.0<\/a>.
TDMR documentation<\/a> and TDMR tutorials<\/a> have been rewritten to account for the simpler interface.<\/p>\r\n

 <\/p>\r\n\r\n

Project Members<\/strong><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n
\"\"<\/td>\r\nDr. Patrick Koch, TH K\u00f6ln<\/td>\r\n\"\"<\/td>\r\nProf. Dr. Wolfgang Konen, TH K\u00f6ln<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n\r\n\r\n\r\n

Publications<\/strong><\/p>\r\n\r\n\r\n\r\n

Publications in the area of Tuned Data Mining (TDM) and TDMR:<\/p>\r\n\r\n\r\n\r\n

<\/a><\/form>

2018<\/h3>

Konen, Wolfgang; Koch, Patrick<\/p>

The TDMR 2.0 Package: Tuned Data Mining in R<\/a> Forschungsbericht<\/span> <\/p>

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) <\/span>Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, <\/span>Nr. 02\/2018, <\/span>2018<\/span>, (Last update: April 2018 (original version: 2012))<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@techreport{Kone18a,
\r\ntitle = {The TDMR 2.0 Package: Tuned Data Mining in R},
\r\nauthor = {Wolfgang Konen and Patrick Koch},
\r\nurl = {http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Kone12a.d\/Kone12a.pdf},
\r\nyear  = {2018},
\r\ndate = {2018-04-01},
\r\nnumber = {02\/2018},
\r\naddress = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
\r\ninstitution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
\r\nnote = {Last update: April 2018 (original version: 2012)},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {techreport}
\r\n}
\r\n<\/pre><\/div>
Schlie\u00dfen<\/a><\/p><\/div>