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Determining k in k-means clustering by exploiting attribute distributions

Bocking, Oscar (2018) Determining k in k-means clustering by exploiting attribute distributions. Bachelor's Thesis, Artificial Intelligence.


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Methods for estimating the natural number of clusters (k) in a data set traditionally rely on the distance between points. In this project, an alternative was investigated: exploiting the distribution of informative nominal attributes over the clusters with a chi-squared test of independence, to see which value of k partitions the data in a way that is least likely to be random. Artificial data sets are used to assess the strategy's performance and viability in comparison to a well-established distance-based method. Results indicate that the proposed strategy has a tendency to overestimate k, and only performs consistently with some types of attribute. Despite this, it has value as a heuristic method when attributes are available due to non-reliance on distance information.

Item Type: Thesis (Bachelor's Thesis)
Supervisor name: Schomaker, L.R.B.
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 13 Jul 2018
Last Modified: 20 Jul 2018 11:36

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