We propose a method of using clustering techniques to partition a set of orders. We define the term order as a sequence of objects that are sorted according to some property, such as size, preference, or price. These orders are useful for, say, carrying out a sensory survey. We propose a method called the k-o'means method, which is a modified version of a k-means method, adjusted to handle orders. We compared our method with the traditional clustering methods, and analyzed its characteristics. We also applied our method to a questionnaire survey data on people's preferences in types of sushi (a Japanese food). © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Kamishima, T., & Fujiki, J. (2003). Clustering orders. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2843, 194–207. https://doi.org/10.1007/978-3-540-39644-4_17
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