An application of the generalised K-means algorithm in decision-making processes

Wu, Hsin-Hung; Shieh, Jiunn I.; Liao, Anthony Y. H.; Lin, Shih Yen

doi:10.1504/ijor.2008.016152

Cited by 7 publications

(9 citation statements)

References 8 publications

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“…K-means method belonging to one of non-hierarchical methods is a very popular approach for classifi cation due to its simplicity of implementation and fast execution [5,11]. This approach has been widely applied to various areas, such as market segmentation, pattern recognition, Downloaded by [Ondokuz Mayis Universitesine] at 23:00 03 November 2014 information retrieval, decision-making, and so on [3,7,11].…”

Section: Review Of K-means Methodsmentioning

confidence: 99%

“…This approach has been widely applied to various areas, such as market segmentation, pattern recognition, Downloaded by [Ondokuz Mayis Universitesine] at 23:00 03 November 2014 information retrieval, decision-making, and so on [3,7,11]. The formula of K-means method typically expressed by Euclidean distance is depicted below.…”

Section: Review Of K-means Methodsmentioning

confidence: 99%

“…Two major steps are involved in K-means method [5,9,11]. First, the assignment step where the instances are placed in the closest class.…”

Section: Review Of K-means Methodsmentioning

confidence: 99%

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A case study of applying LRFM model and clustering techniques to evaluate customer values

Kao¹,

Wu²,

Chen

et al. 2011

Journal of Statistics and Management Systems

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A case study of applying LRFM (length, recency, frequency, and monetary) model and clustering techniques in evaluating an outfi tter's customer values is presented. Self-organizing maps is fi rst used to determine the best number of clusters and then K-means method is applied to classify 551 customers into twelve clusters when L, R, F, and M are the segmenting variables. The results show that Cluster 5 might be the most important cluster because the average values of L, R, F, and M are well above the averages. In contrast, the customers in Clusters 7 and 10 have low contributions since L, R, F, and M values are below the average values. As a result, with the applications of LRFM model and clustering techniques, the outfi tter can allocate and utilize resources eff ectively and eff iciently to fi rst identify high-value and profi t potential customers and then design diff erent marketing strategies to maximize its profi ts for diff erent types of clusters.

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Section: Review Of K-means Methodsmentioning

confidence: 99%

Section: Review Of K-means Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A case study of applying LRFM model and clustering techniques to evaluate customer values

Kao¹,

Wu²,

Chen

et al. 2011

Journal of Statistics and Management Systems

View full text Add to dashboard Cite

show abstract

“…K-means method, a very popular non-hierarchical approach for data clustering due to its simplicity of implementation and fast execution, uses Euclidean distance to measure the distance between two points (Yoon and Hwang 1995;Wu et al 2008). There are two major steps of K-means method (Wu et al 2008).…”

Section: Means Methodsmentioning

confidence: 99%

“…There are two major steps of K-means method (Wu et al 2008). First, the assignment step where the instances are placed in the closest class.…”

Section: Means Methodsmentioning

confidence: 99%

Using K-means method and spectral clustering technique in an outfitter’s value analysis

Chang

Huang

2009

Qual Quant

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Applying RFM Model and K-Means Method in Customer Value Analysis of an Outfitter

Chang

Lo³

2009

Global Perspective for Competitive Enterprise, Economy and Ecology

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An application of the generalised K-means algorithm in decision-making processes

Cited by 7 publications

References 8 publications

A case study of applying LRFM model and clustering techniques to evaluate customer values

A case study of applying LRFM model and clustering techniques to evaluate customer values

Using K-means method and spectral clustering technique in an outfitter’s value analysis

Applying RFM Model and K-Means Method in Customer Value Analysis of an Outfitter

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