Contents
How do outliers affect the distribution?
Outlier Affect on variance, and standard deviation of a data distribution. In a data distribution, with extreme outliers, the distribution is skewed in the direction of the outliers which makes it difficult to analyze the data.
Can you have outliers in a normal distribution?
If you expect a normal distribution of your data points, for example, then you can define an outlier as any point that is outside the 3σ interval, which should encompass 99.7% of your data points. In this case, you’d expect that around 0.3% of your data points would be outliers.
How dO you identify outliers?
The simplest way to detect an outlier is by graphing the features or the data points. Visualization is one of the best and easiest ways to have an inference about the overall data and the outliers. Scatter plots and box plots are the most preferred visualization tools to detect outliers.
Is the k-means algorithm sensitive to outliers?
The algorithm aims to minimize the squared Euclidean distances between the observation and the centroid of cluster to which it belongs. But sometime K-Means algorithm does not give best results. It is sensitive to outliers.
How can I reduce the effect of outliers?
Ignore the outlier removal and just use more robust variations of K-means, e.g. K-medoids or K-Medians, to reduce the effect of outliers. The last but not the least is to care about the dimensionality of the data. K-Means is not a proper algorithm for high dimensional setting and needs a dimensionality reduction step beforehand.
How to find an outlier in a cluster?
First I perform the algorithm and choose those objects as possible outliers which have a big distance to their cluster center.
How does k-means reduce the loss function?
Therefore, K-means would reduce the loss function by choosing the outlier itself to be one of centroids, and placing the other centroid somewhere in the middle of the remaining data. This configuration is clearly not representative of the the underlying distribution, but a pathological situation caused by the presence of a single outlier.