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- Publisher Website: 10.1093/imaman/15.1.13
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Article: Hidden Markov models and their applications to customer relationship management
Title | Hidden Markov models and their applications to customer relationship management |
---|---|
Authors | |
Keywords | Customers Classification Hidden Markov Model Markov Chain Stationary Distribution Transition Probability |
Issue Date | 2004 |
Publisher | Oxford 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? |
Abstract | Hidden 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 Identifier | http://hdl.handle.net/10722/156151 |
ISSN | 2023 Impact Factor: 1.9 2023 SCImago Journal Rankings: 0.730 |
DC Field | Value | Language |
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dc.contributor.author | Ching, WK | en_US |
dc.contributor.author | Ng, MK | en_US |
dc.contributor.author | Wong, KK | en_US |
dc.date.accessioned | 2012-08-08T08:40:36Z | - |
dc.date.available | 2012-08-08T08:40:36Z | - |
dc.date.issued | 2004 | en_US |
dc.identifier.citation | Ima Journal Management Mathematics, 2004, v. 15 n. 1, p. 13-24 | en_US |
dc.identifier.issn | 1471-678X | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/156151 | - |
dc.description.abstract | Hidden 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.language | eng | en_US |
dc.publisher | Oxford University Press. The Journal's web site is located at http://imaman.oxfordjournals.org/ | en_US |
dc.relation.ispartof | IMA Journal Management Mathematics | en_US |
dc.subject | Customers Classification | en_US |
dc.subject | Hidden Markov Model | en_US |
dc.subject | Markov Chain | en_US |
dc.subject | Stationary Distribution | en_US |
dc.subject | Transition Probability | en_US |
dc.title | Hidden Markov models and their applications to customer relationship management | en_US |
dc.type | Article | en_US |
dc.identifier.email | Ching, WK:wching@hku.hk | en_US |
dc.identifier.authority | Ching, WK=rp00679 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1093/imaman/15.1.13 | en_US |
dc.identifier.scopus | eid_2-s2.0-2942617084 | en_US |
dc.identifier.hkuros | 88734 | - |
dc.identifier.volume | 15 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.spage | 13 | en_US |
dc.identifier.epage | 24 | en_US |
dc.publisher.place | United Kingdom | en_US |
dc.identifier.scopusauthorid | Ching, WK=13310265500 | en_US |
dc.identifier.scopusauthorid | Ng, MK=34571761900 | en_US |
dc.identifier.scopusauthorid | Wong, KK=7404759138 | en_US |
dc.identifier.issnl | 1471-678X | - |