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dc.contributor.authorCamilleri, Liberato-
dc.contributor.authorSpiteri, Luke-
dc.contributor.authorCamilleri, Maureen-
dc.identifier.citationCamilleri, L., Spiteri, L., & Camilleri, M. (2019). Investigating the factors which affect the performance of the EM algorithm in Latent class models. Industrial Simulation Conference, Lisbon. 5-10.en_GB
dc.description.abstractLatent class models have been used extensively in market segmentation to divide a total market into market groups of consumers who have relatively similar product needs and preferences. The advantage of these models over traditional clustering techniques lies in simultaneous estimation and segmentation, which is carried out using the EM algorithm. The identification of consumer segments allows target-marketing strategies to be developed. The data comprises the rating responses of 262 respondents to 24 laptop profiles described by four item attributes including the brand, price, random access memory (RAM) and the screen size. Using the facilities of R Studio, two latent class models were fitted by varying the number of clusters from 2 to 3. The parameter estimates obtained from these two latent class models were used to simulate a number of data sets for each cluster solution to be able to conduct a Monte-Carlo study, which investigates factors that have an effect on segment membership and parameter recovery and affect computational effort.en_GB
dc.publisherThe European Multidisciplinary Society for Modelling and Simulation Technologyen_GB
dc.subjectMarket segmentationen_GB
dc.subjectExpectation-maximization algorithmsen_GB
dc.subjectMonte Carlo methoden_GB
dc.titleInvestigating the factors which affect the performance of the EM algorithm in Latent class modelsen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencenameIndustrial Simulation Conference (ISC'2019)en_GB
dc.bibliographicCitation.conferenceplaceLisbon, Portugal, 05-07/06/2019en_GB
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