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

Chang, En Chi; Huang, Shian Chang; Wu, Hsin-Hung

doi:10.1007/s11135-009-9240-0

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…On the contrary, cluster analysis which is one of the data mining techniques can be applied to divide the entire medical staffs into an appropriate number of groups based on their similarities. 11 In marketing applications, the values of different customer groups after cluster analysis can be calculated and evaluated to provide the management useful decisional information for resources allocation. 11 - 15 The purpose of this study is to identify medical staffs with high burnout by cluster analysis such that the hospital management can take actions to improve the resilience, further reduce the potential medical errors, and, eventually, enhance the patient safety.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

Lee

Huang

et al. 2016

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This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a regional teaching hospital in Taiwan. The number of effective questionnaires including the entire staffs such as physicians, nurses, technicians, pharmacists, medical administrators, and respiratory therapists is 680. The results show that 8 clusters are generated by kernel k-means method. Employees in clusters 1, 4, and 5 are relatively in good conditions, whereas employees in clusters 2, 3, 6, 7, and 8 need to be closely monitored from time to time because they have relatively higher degree of burnout. When employees with higher degree of burnout are identified, the hospital management can take actions to improve the resilience, reduce the potential medical errors, and, eventually, enhance the patient safety. This study also suggests that the hospital management needs to keep track of medical staffs’ fatigue conditions and provide timely assistance for burnout recovery through employee assistance programs, mindfulness-based stress reduction programs, positivity currency buildup, and forming appreciative inquiry groups.

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Section: Introductionmentioning

confidence: 99%

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

Lee

Huang

et al. 2016

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“…Despite the advantages of k-means as a traditional cluster analysis method, this technique is sensitive to the choice of a starting point for partitioning the items into K initial clusters. Due to the weakness of the K-means method, prior literature proposes to adopt a two-staged clustering method [72]. In this regard, this study applied the K-means technique to determine the clustering boundaries from the results of the supervised methods, DT or MLP.…”

Section: K-means Clusteringmentioning

confidence: 99%

Identifying central and peripheral nerve fibres with an artificial intelligence approach

Gil

Girela

Molina‐Azorín

et al. 2018

Applied Soft Computing

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Distinguishing axons from central or peripheral nervous systems (CNS or PNS, respectively) is often a complicated task. The main objective of this work was to facilitate and support the process of automatically distinguishing the different types of nerve fibres by analysing their morphological characteristics. Our approach was based on a multi-level hierarchical classifier architecture that can handle the complexity of directly identifying nerve-fibre groups belonging to either the CNS or the PNS. The approach adopted comprises supervised methods (multilayer perceptron and decision trees), which are responsible for distinguishing the origin of the axons (CNS or PNS), whereas the unsupervised method (K-Means clustering) performs nerve fibre clustering based on similar characteristics for both the CNS and PNS. Our experiments produced results with an accuracy higher than 88%. Our findings suggest that the development and implementation of a multi-level system improves automation capabilities and increases accuracy in the classification of nerves. Furthermore, our architecture allows for generalisation and flexibility, which can subsequently be extended to other biological control systems.

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“…Self-organizing maps method has the advantages over other traditional cluster analysis methods but it does not provide measures for validation for the cluster analysis results [ 18 ]. In contrast, K -means method is very sensitive to the choice of a starting point to partition the items into K initial clusters [ 5 , 19 , 20 ]. Therefore, a two-stage approach combining SOM and K -means method has been proposed and widely applied to improve the weakness [ 8 , 20 , 21 ].…”

Section: Literature Review Of Cluster Analysis Lrfm Model and Cumentioning

confidence: 99%

“…LRFM model was developed based on RFM model, which is a well-known method to analyze customer values for market segmentation [ 19 , 22 ]. The definition of RFM model is as follows [ 23 , 24 ]: recency is defined as the number of periods since the last purchase, that is, days or month.…”

Section: Literature Review Of Cluster Analysis Lrfm Model and Cumentioning

confidence: 99%

“…Thus, each customer based on RFM model can be represented by one of 125 RFM cells, namely, 555, 554, 553, …, 111 [ 22 , 25 ]. Chang et al [ 19 ], on the other hand, use the original data rather than the coded number to perform RFM model. The definitions are as follows: recency is the time length since the most recent purchase; frequency computes the number of purchases during the same period of time; and monetary refers to the total amount of money spent on all purchases given the same period of time.…”

Section: Literature Review Of Cluster Analysis Lrfm Model and Cumentioning

confidence: 99%

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Analyzing Patients’ Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

Lin

Liu

2014

The Scientific World Journal

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This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs.

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

Abstract: K-means method, Spectral clustering technique, Cluster quality assessment, Marketing strategy, Customer value,

Cited by 25 publications

References 13 publications

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan

Identifying central and peripheral nerve fibres with an artificial intelligence approach

Analyzing Patients’ Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

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