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Just want to understand one thing. Let's say for any data set I select k=20 and generate LOF for each point and then I show all the points in the descending order of its LOF. Now when I am analyzing the data I can choose the range till which I think the data is an outlier(as per knowledge of the domain)

Do you think this helps?? I just me as now I don't have to worry about the value of k and I am using my domain knowledge to analyze the outliers as per the LOF ranking.

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This seems like a viable approach. The threshold for distinguishing outliers from inliers needs to be entirely determined by you, but using your domain knowledge this seems to work out.

I'd like to refer you to the original paper describing LOF in order to understand its disadvantages (although wikipedia is a good start as well).

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