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Does k-means fail with outliers?
Clustering performed by K-means and MAP-DP for spherical, synthetic Gaussian data, with outliers. K-means fails to find a meaningful solution, because, unlike MAP-DP, it cannot adapt to different cluster densities, even when the clusters are spherical, have equal radii and are well-separated.
What are the disadvantages of K-means algorithm?
The most important limitations of Simple k-means are: The user has to specify k (the number of clusters) in the beginning. k-means can only handle numerical data. k-means assumes that we deal with spherical clusters and that each cluster has roughly equal numbers of observations.
Is the k-means algorithm used for outlier detection?
We propose a k-means-type algorithm by incorporating an additional “cluster” into the objective function. The algorithm is able to provide data clustering and outlier detection simultaneously. Outliers are not used in the cluster center calculation. Experiments on synthetic and real data show that the algorithm performs well. Abstract
What are the parameters of the k means algorithm?
Chawla and Gionis [6] proposed the k -means– algorithm to provide data clustering and outlier detection simultaneously. The k -means– algorithm requires two parameters: k and l, which specify the desired number of clusters and the desired number of top outliers, respectively.
How is k-means used for data clustering?
We study the problem of data clustering with outlier detection. We propose a k-means-type algorithm by incorporating an additional “cluster” into the objective function. The algorithm is able to provide data clustering and outlier detection simultaneously. Outliers are not used in the cluster center calculation.
How does the KMOR algorithm work for clustering?
Given a desired number of clusters k, the KMOR algorithm partitions the dataset into k+1groups, which include kclusters and a group of outliers that cannot fit into the kclusters. Unlike most existing clustering algorithms with outlier detection, the KMOR algorithm assigns all outliers into a group naturally during the clustering process.