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    <p>Can't you just try sum |xi - yi| instead if (xi - yi)^2 in your code, and see if it makes much difference ?</p> <blockquote> <p>I can't have a graph which will give some idea about the correctness of my algorithm.</p> </blockquote> <p>A couple of possibilities:</p> <ul> <li>look at some points midway between 2 clusters <em>in detail</em></li> <li>vary k a bit, see what happens (what is your k ?)</li> <li>use <a href="http://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow noreferrer">PCA</a> to map 30d down to 2d; see the plots under <a href="https://stackoverflow.com/questions/6645895/calculating-the-percentage-of-variance-measure-for-k-means">calculating-the-percentage-of-variance-measure-for-k-means</a>, also <a href="https://stackoverflow.com/questions/tagged/pca">SO questions/tagged/pca</a></li> </ul> <p>By the way, <a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.html" rel="nofollow noreferrer">scipy.spatial.cKDTree</a> can easily give you say 3 nearest neighbors of each point, in p=2 (Euclidean) or p=1 (Manhattan, L1), to look at. It's fast up to ~ 20d, and with early cutoff works even in 128d.</p> <p><hr> Added: I like <a href="http://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow noreferrer">Cosine distance</a> in high dimensions; see <a href="https://stats.stackexchange.com/questions/29627/euclidean-distance-is-usually-not-good-for-sparse-data/30235#30235">euclidean-distance-is-usually-not-good-for-sparse-data</a> for why.</p>
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