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Simple K-Medoids Partitioning Algorithm for Mixed Variable Data

1
Agribusiness Department, Sultan Ageng Tirtayasa University, Jl. Raya Jakarta Km. 4 Serang, Banten 42118, Indonesia
2
Institute of Statistics, University of Natural Resources and Life Sciences, Peter Jordan Straße 82, 1180 Vienna, Austria
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(9), 177; https://doi.org/10.3390/a12090177
Received: 15 July 2019 / Revised: 15 August 2019 / Accepted: 20 August 2019 / Published: 24 August 2019
(This article belongs to the Special Issue Clustering Algorithms and Their Applications)
A simple and fast k-medoids algorithm that updates medoids by minimizing the total distance within clusters has been developed. Although it is simple and fast, as its name suggests, it nonetheless has neglected local optima and empty clusters that may arise. With the distance as an input to the algorithm, a generalized distance function is developed to increase the variation of the distances, especially for a mixed variable dataset. The variation of the distances is a crucial part of a partitioning algorithm due to different distances producing different outcomes. The experimental results of the simple k-medoids algorithm produce consistently good performances in various settings of mixed variable data. It also has a high cluster accuracy compared to other distance-based partitioning algorithms for mixed variable data. View Full-Text
Keywords: cluster; distance; partitioning; k-medoids; mixed variable data cluster; distance; partitioning; k-medoids; mixed variable data
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Budiaji, W.; Leisch, F. Simple K-Medoids Partitioning Algorithm for Mixed Variable Data. Algorithms 2019, 12, 177.

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