Abstract
K-Means is one of the most important data mining techniques for scientists who want to analyze their data. But K-Means has the disadvantage that it is unable to handle noise data points. This paper proposes a technique that can be applied to the k-means Clustering result to exclude noise data points. We refer to it as KMN (short for K-Means with Noise). This technique is compatible with the different strategies to initialize k-means and determine the number of clusters. Moreover, it is completely parameter-free. The technique has been tested on artificial and real data sets to demonstrate its performance in comparison with other noise-excluding techniques for k-means.
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Notes
- 1.
For additional information on the concept of voronoi cells we refer to [13] or the Wikipedia article.
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Schelling, B., Plant, C. (2018). KMN - Removing Noise from K-Means Clustering Results. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_11
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DOI: https://doi.org/10.1007/978-3-319-98539-8_11
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