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Why is k-means non convex?
[Source: Wikipedia.] The k-means algorithm, in its basic form, is like making little circular paper cutouts and using them to cover the data points. We can change the quantity and size and position of our paper cut-outs, but they are still round and, thus, these non-convex shapes evade us.
Is k-means a convex problem?
k-means clustering is a popular approach to clustering. It is easy to imple- ment and intuitive but has the disadvantage of being sensitive to initializa- tion due to an underlying non-convex optimization problem.
Is the k-means objective function convex?
K-means will always produce convex clusters, thus it can only work if clusters can be linearly separated. The k-means objective function fails sometimes even in cases that are linearly separable and appear easy, see the examples in Figure 5, where k-means fails once the data set gets more and more dilated.
What is the problem of the k-means method?
k-means has trouble clustering data where clusters are of varying sizes and density. To cluster such data, you need to generalize k-means as described in the Advantages section. Clustering outliers. Centroids can be dragged by outliers, or outliers might get their own cluster instead of being ignored.
Which function is used in K means algorithm?
K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.
What is K means good for?
Business Uses. The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.
Is the k-means not converged with a non convex function?
K-Means is not converged with non-convex shape data set .Nothing relate with convex function,sorry. Browse other questions tagged machine-learning data-mining clustering k-means or ask your own question.
Which is situation cause the non-convex problem?
In all articles I’ve read say “K-means is not converge with non-convex function”.I just know that convex function is the function that able to find the global minimum. However , I do not understand how this relate to K-Means algorithm. Which situation cause the non-convex problem ?
Can a convex function approximate a non convex function?
•Convex functions can’t approximate non-convex ones well. •Neural nets also have many symmetric configurations •For example, exchanging intermediate neurons •This symmetry means they can’t be convex. Why?
What makes non-convex optimization hard Cornell University?
Non-Convex Optimization CS6787 Lecture 7—Fall 2017 Review —We’ve covered many methods •Stochastic gradient descent •Mini-batching •Momentum •Variance reduction •Nice convergence proofs that give us a rate •But only for convex problems! Non-Convex Problems •Anything that’s not convex What makes non-convex optimization hard?