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I will make k-means clustering for a segmentation project.But I know that this algorithm is effectable from outliers.Which way should I perform for detecting outliers before doing k-means algorithm? For example should I perform anomaly detection algorithm on the data set to detect outliers and after detecting and excluding outliers ,performing k-means algorithm for more stable clustering? Or Should I detect outliers by using k-means algorithm itself by finding average distances and finding the outliers beyond these distances? What approach should be taken for detecting outliers and making more stable clustering? I need your suggestions.

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You could try any of the standard outlier methods, such as kNN, LOF, LOOP, INFLO, etc.

There are also robust k-means variations such as k-means--.

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Detect outlier first, if you data set maybe contain outlier.

Try the isolationForest method, it's fast and efficient to detect the outliers.

update:

isolationForest

papers:https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/tkdd11.pdf

python code:http://scikit-learn.org/dev/modules/generated/sklearn.ensemble.IsolationForest.html

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  • $\begingroup$ I'd be interested to hear more. $\endgroup$ – rolando2 Apr 7 '17 at 11:56
  • $\begingroup$ which algorithm or method should be used for detecting outliers ? Thanks $\endgroup$ – hncltpcgl Apr 7 '17 at 12:06
  • $\begingroup$ Not my downvote, but the link to the papers is dead. $\endgroup$ – G5W Apr 10 '17 at 18:03
  • $\begingroup$ I can visit it. Maybe you should use proxy to visit Chinese websites. $\endgroup$ – wolfe Apr 11 '17 at 5:36
  • $\begingroup$ Link to the PDF is alive and accessible (EU, Greece) $\endgroup$ – Dimitris S. Jun 9 '18 at 21:01

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