A comparison of several cluster algorithms on artificial binary data [Part 2]. Scenarios from travel market segmentation. Part 2 (Addition to Working Paper No. 7).

Dolnicar, Sara and Leisch, Friedrich and Steiner, Gottfried and Weingessel, Andreas (1998) A comparison of several cluster algorithms on artificial binary data [Part 2]. Scenarios from travel market segmentation. Part 2 (Addition to Working Paper No. 7). Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science", 19. SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, Vienna.

[img]
Preview
PDF
document.pdf

Download (176kB)

Abstract

The search for clusters in empirical data is an important and often encountered research problem. Numerous algorithms exist that are able to render groups of objects or individuals. Of course each algorithm has its strengths and weaknesses. In order to identify these crucial points artificial data was generated - based primarily on experience with structures of empirical data - and used as benchmark for evaluating the results of numerous cluster algorithms. This work is an addition to SFB Working Paper No. 7 where hard competitive learning (HCL), neural gas (NGAS), k-means and self organizing maps (SOMs) were compared. Since the artificial data scenarios and the evaluation criteria used remained the same, they are not explained in this work, where the results of five additional algorithms are evaluated. (author's abstract)

Item Type: Paper
Keywords: Fremdenverkehr / Marktsegmentierung / Binärdaten / Cluster-Analyse
Divisions: Departments > Informationsverarbeitung u Prozessmanag. > Produktionsmanagement > Taudes
Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Departments > Marketing > Service Marketing und Tourismus
Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft
Depositing User: Repository Administrator
Date Deposited: 22 Mar 2002 11:39
Last Modified: 22 Oct 2019 00:41
URI: https://epub.wu.ac.at/id/eprint/112

Actions

View Item View Item

Downloads

Downloads per month over past year

View more statistics