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Article: Hidden Markov models and their applications to customer relationship management

TitleHidden Markov models and their applications to customer relationship management
Authors
KeywordsCustomers Classification
Hidden Markov Model
Markov Chain
Stationary Distribution
Transition Probability
Issue Date2004
PublisherOxford University Press. The Journal's web site is located at http://imaman.oxfordjournals.org/
Citation
Ima Journal Management Mathematics, 2004, v. 15 n. 1, p. 13-24 How to Cite?
AbstractHidden Markov models (HMMs) are widely used in science, engineering and many other areas. In a HMM, there are two types of states: hidden states and observable states. Here we propose a HMM via the framework of a Markov chain model. Simple estimation methods for the transition probabilities among the hidden states are discussed. The estimation methods are better than the traditional EM algorithm in both the quality of estimation and the computational complexity. We then apply the model to classify the customers of a computer service company which is an important task in the customer relationship management. Numerical examples are given to illustrate the usefulness of the model by using a real-world data set.
Persistent Identifierhttp://hdl.handle.net/10722/156151
ISSN
2015 Impact Factor: 0.878
2015 SCImago Journal Rankings: 0.351

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_US
dc.contributor.authorNg, MKen_US
dc.contributor.authorWong, KKen_US
dc.date.accessioned2012-08-08T08:40:36Z-
dc.date.available2012-08-08T08:40:36Z-
dc.date.issued2004en_US
dc.identifier.citationIma Journal Management Mathematics, 2004, v. 15 n. 1, p. 13-24en_US
dc.identifier.issn1471-678Xen_US
dc.identifier.urihttp://hdl.handle.net/10722/156151-
dc.description.abstractHidden Markov models (HMMs) are widely used in science, engineering and many other areas. In a HMM, there are two types of states: hidden states and observable states. Here we propose a HMM via the framework of a Markov chain model. Simple estimation methods for the transition probabilities among the hidden states are discussed. The estimation methods are better than the traditional EM algorithm in both the quality of estimation and the computational complexity. We then apply the model to classify the customers of a computer service company which is an important task in the customer relationship management. Numerical examples are given to illustrate the usefulness of the model by using a real-world data set.en_US
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://imaman.oxfordjournals.org/en_US
dc.relation.ispartofIMA Journal Management Mathematicsen_US
dc.subjectCustomers Classificationen_US
dc.subjectHidden Markov Modelen_US
dc.subjectMarkov Chainen_US
dc.subjectStationary Distributionen_US
dc.subjectTransition Probabilityen_US
dc.titleHidden Markov models and their applications to customer relationship managementen_US
dc.typeArticleen_US
dc.identifier.emailChing, WK:wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1093/imaman/15.1.13en_US
dc.identifier.scopuseid_2-s2.0-2942617084en_US
dc.identifier.hkuros88734-
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.spage13en_US
dc.identifier.epage24en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US
dc.identifier.scopusauthoridNg, MK=34571761900en_US
dc.identifier.scopusauthoridWong, KK=7404759138en_US

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