federico vaggi <vaggi.feder...@gmail.com> 於 2019年6月7日 週五 上午1:08寫道：
> k-means isn't a convex problem, unless you freeze the initialization, you > are going to get very different solutions (depending on the dataset) with > different initializations. > > Nope, I specify the random_state=0. u can try it. >>> x = np.array([[1,2],[2,3]]) >>> y = np.array([[3,4],[4,5],[5,6]]) >>> z = np.append(x,y, axis=0) >>> from sklearn.cluster import MiniBatchKMeans as MBK >>> m = MBK(random_state=0, n_clusters=2) >>> m.fit(x) ; m.labels_ array([1,0], dtype=int32) <-- (1-a) >>> m.partial_fit(y) ; m.labels_ array([0,0,0], dtype=int32) <-- (1-b) >>> m = MBK(random_state=0, n_clusters=2) >>> m.partial_fit(x) ; m.labels_ array([0,1], dtype=int32) <-- (2-a) >>> m.partial_fit(y) ; m.labels_ array([1,1,1], dtype=int32) <-- (2-b) 1-a,1-b and 2-a, 2-b are all different, especially the members of each cluster. I'm just confused about what usage of partial_fit and fit is the suitable(reasonable?) way to cluster incrementally? thx
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