an algorithm for clustering visually separable clusters

I have visualized a dataset in 2D after employing PCA. 1 dimension is time and the Y dimension is First PCA component. As figure shows, there is relatively good separation between points (A, B). But unfortunately clustering methods (DBSCAN, SMO, KMEANS, Hierarchical) are not able to cluster these points in 2 clusters. As you see in section A there is a relative continuity and this continuous process is finished and Section B starts and there is rather big gap in comparison to past data between A and B.

I will be so grateful if you can introduce me any method and algorithm (or devising any metric from data considering its distribution) to be able to do separation between A and B without visualization. Thank you so much.

an algorithm for clustering visually separable clusters


If PCA gives you a good separation, you can just try to cluster after projecting your data through you PCA eigenvectors. If you don't want to use PCA, then you will need anyway a substitute data projection method, because failing clustering methods imply that your data is not separable in the original dimensions. You can take a look at non linear clustering methods such as the kernel based ones or spectral clustering for example. Or to define your own non-euclidian metric, which is finally just another data projection method.

But using PCA clearly seems to be the best fit in your case (Occam razor : use the simplest model that fits your data).

Category: machine learning Time: 2016-07-29 Views: 0

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