A multicriteria ordered clustering algorithm to determine precise or disjunctive partitions

Mohamed Ayman Boujelben and Yves De Smet

International Journal of Multicriteria Decision Making, 2016, vol. 6, issue 2, 157-187

Abstract: We consider multicriteria clustering problems where the groups are ordered from the best to the worst. An approach relying on the principles of the k-means algorithm and disjunctive sorting based on evidence theory (DISSET) method is proposed for the detection of ordered clusters. The distinctive feature of this method is that it allows to obtain both precise and disjunctive partitions. In such situation, the actions can be assigned even to pair of groups (and not only to precise clusters). The decision maker is assumed to provide the following inputs: an evaluation table, the desired number of clusters and a valued preference model (obtained for instance by PROMETHEE method). The method is illustrated on two real examples: the Human Development Index (HDI-2013) and the Logistics Performance Index (LPI-2014).

Keywords: multicriteria decision aiding; MCDA; evidence theory; multicriteria ordered clustering; precise partitions; disjunctive partitions; multicriteria decision making; MCDM; k-means clustering; disjunctive sorting; evidence theory; DISSET; PROMETHEE. (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.inderscience.com/link.php?id=77886 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmcdm:v:6:y:2016:i:2:p:157-187

Access Statistics for this article

More articles in International Journal of Multicriteria Decision Making from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().