Statistical and computational trade-offs in kernel k-means

What are the challenges associated with k-means clustering?
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..
What are the limitations of k-means clustering algorithm?
difficult to choose the number of clusters, ��cannot be used with arbitrary distances.sensitive to scaling – requires careful preprocessing.does not produce the same result every time.clever initialization helps finding better results.sensitive to outliers (squared errors emphasize outliers).
What is the concept of kernel and k-means?
Kernel k-means clustering is a powerful tool for unsupervised learning of non-linearly separable data.
Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from non-convexity of the underlying objective function..
There are essentially three stopping criteria that can be adopted to stop the K-means algorithm:
Centroids of newly formed clusters do not change.Points remain in the same cluster.Maximum number of iterations is reached.
Kernel k-means clustering is a powerful tool for unsupervised learning of non-linearly separable data.
Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from non-convexity of the underlying objective function.

Within bayesian statistics for machine learning, kernel methods arise from the assumption of an inner product space or similarity structure on inputs.
For some such methods, such as support vector machines (SVMs), the original formulation and its regularization were not Bayesian in nature.
It is helpful to understand them from a Bayesian perspective.
Because the kernels are not necessarily positive semidefinite, the underlying structure may not be inner product spaces, but instead more general reproducing kernel Hilbert spaces.
In Bayesian probability kernel methods are a key component of Gaussian processes, where the kernel function is known as the covariance function.
Kernel methods have traditionally been used in supervised learning problems where the input space is usually a space of vectors while the output space is a space of scalars.
More recently these methods have been extended to problems that deal with multiple outputs such as in multi-task learning.

Categories

Statistical and computational trade-offs in kernel k-means

What are the challenges associated with k-means clustering?

What are the limitations of k-means clustering algorithm?

What is the concept of kernel and k-means?

There are essentially three stopping criteria that can be adopted to stop the K-means algorithm: