Abstract
We present a new clustering algorithm for handling complexities encountered in analysing data sets of hotel ratings and analyse its performance in a clustering case study. In the setting we address, business constraints and coordinates (among other individual attributes of objects) are unknown and only distances between objects are available to the clustering algorithm, a situation that arises in a wide range of clustering applications. Our algorithm constitutes an application of meta-analytics, in which we tailor a metaheuristic procedure to address a challenging problem at the intersection of predictive and prescriptive analytics. Our work builds on and extends the ideas of our clustering algorithm introduced in previous work which employs the Tabu Search metaheuristic to assure clusters exhibit a property we call cohesiveness. The special characteristics of the present hotel classification problem are handled by integrating our previous method with a new form of hierarchical clustering. Our computational analysis discloses that our algorithm obtains clusters that exhibit greater cohesiveness than those produced by the classical K-means method.
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Notes
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- 2.
The terms “intensification” and “diversification”, now widely used in many metaheuristic algorithms, were originally introduced in Tabu Search.
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All data sets can be downloaded from http://times.cs.uiuc.edu/~wang296/Data.
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Acknowledgements
We are indebted to a reviewer for insightful critical observations and suggestions that have helped to improve our chapter. The authors would like to thank our student team including Zheng Xu and Fang Yu for their efforts in implementing the algorithm, data preparation, and data analysis. This work was partially supported by the China Intelligent Urbanization Co-Creation Center [grant number CIUC20150011] and was also supported in part by the Key Laboratory of International Education Cooperation of Guangdong University of Technology.
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Cao, B., Rego, C., Glover, F. (2019). Hotel Classification Using Meta-Analytics: A Case Study with Cohesive Clustering. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_21
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