KMN - Removing Noise from K-Means Clustering Results
作者： Benjamin Schelling , Claudia Plant
DOI: 10.1007/978-3-319-98539-8_11
关键词: Cluster analysis 、 Data set 、 Computer science 、 Noise 、 Pattern recognition 、 k-means clustering 、 Artificial intelligence 、 Data point
摘要: K-Means is one of the most important data mining techniques for scientists who want to analyze their data. But has disadvantage that it unable handle noise points. This paper proposes a technique can be applied k-means Clustering result exclude We refer as KMN (short with Noise). compatible different strategies initialize and determine number clusters. Moreover, completely parameter-free. The been tested on artificial real sets demonstrate its performance in comparison other noise-excluding k-means.
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