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In this chapter the application of clustering techniques for grouping students according to their learning style preferences is considered. Such groups are evaluated by disparate validation criteria and the usage of different validation techniques is discussed. Experiments were conducted for different sets of real and artificially generated data on students' learning styles and the indices: Dunn's Index, Davies-Bouldin Index, SD Validity Index as well as the S_Dbw Validity Index are compared. From the experiment results some indications concerning the best validating criteria, as well as optimal clustering schema, are presented.<\/p>","DOI":"10.4018\/ijoci.2012100102","type":"journal-article","created":{"date-parts":[[2014,3,13]],"date-time":"2014-03-13T11:10:50Z","timestamp":1394709050000},"page":"19-38","source":"Crossref","is-referenced-by-count":0,"title":["Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems"],"prefix":"10.4018","volume":"3","author":[{"given":"Danuta","family":"Zakrzewska","sequence":"first","affiliation":[{"name":"Technical University of Lodz, Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijoci.2012100102-0","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-006-9012-7"},{"key":"ijoci.2012100102-1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011143116306"},{"key":"ijoci.2012100102-2","doi-asserted-by":"crossref","unstructured":"Cha, H. 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