Notice of Retraction Dynamic clustering of data with modified K-prototype algorithm
Author(s) -
Anupama Chadha
Publication year - 2019
Publication title -
aptikom journal on computer science and information technologies
Language(s) - English
Resource type - Journals
eISSN - 2528-2425
pISSN - 2528-2417
DOI - 10.11591/aptikom.j.csit.85
Subject(s) - cluster analysis , notice , computer science , categorical variable , data mining , algorithm , k means clustering , partition (number theory) , cure data clustering algorithm , constraint (computer aided design) , data stream clustering , simplicity , fuzzy clustering , mathematics , artificial intelligence , machine learning , political science , law , philosophy , geometry , epistemology , combinatorics
Notice of RetractionAfter careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ij.aptikom@gmail.com.Clustering of mixed numerical and categorical data has become a challenge now a days. A number of algorithms dealing with mixed data have been proposed. Speed and simplicity are the two major features that have made the K-Prototype algorithm a famous partition based clustering algorithm. This algorithm has a constraint of providing the value of K initially and sometimes predicting the optimum number of clusters in advance becomes practically impossible. In this paper, a new algorithm based on the K-Prototype algorithm for clustering mixed data with advanced features for automatic generation of appropriate number of clusters is presented.
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