## Will K-means always converge?

The algorithm does not guarantee convergence to the global optimum. The result may depend on the initial clusters. As the algorithm is usually fast, it is common to run it multiple times with different starting conditions.

## Is Kmeans always guaranteed to converge?

The algorithm always converges (by-definition) but not necessarily to global optimum. The algorithm may switch from centroid to centroid but this is a parameter of the algorithm ( precision , or delta ). This is sometimes refered as “cycling”.

## Why K-means guaranteed to converge?

It proves mathematically that the iterated running of finding the centers in k-means is converges. The reason is that: In every iteration of k-means, the sum-of-distances to the center is reduced. This is because of how the center is selected (center of cluster is the the mean of each cluster nodes) in each iteration.

## Is K-means a supervised learning algorithm?

K-Means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning.

## Is K-means sensitive to outliers in data?

The K-means clustering algorithm is sensitive to outliers, because a mean is easily influenced by extreme values. The group of points in the right form a cluster, while the rightmost point is an outlier.

## What is K-means algorithm with example?

K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. It assumes that the number of clusters are already known. It is also called flat clustering algorithm. The number of clusters identified from data by algorithm is represented by ‘K’ in K-means.

## What is cluster algorithm?

Cluster analysis, or clustering, is an unsupervised machine learning task. It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space.

## What is the basic K-means algorithm?

K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.

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## Why K-means ++ is better?

K-means++ is the algorithm which is used to overcome the drawback posed by the k-means algorithm. This algorithm guarantees a more intelligent introduction of the centroids and improves the nature of the clustering.

## Why we use K-means ++?

To overcome the above-mentioned drawback we use K-means++. This algorithm ensures a smarter initialization of the centroids and improves the quality of the clustering. Apart from initialization, the rest of the algorithm is the same as the standard K-means algorithm.

## What is vanilla K?

K-means is an algorithm to assign clusters to different points. The algorithm takes two main inputs, a list of points and a fixed number of clusters and provides two different outputs, a cluster assignment for each point, a cluster center for each of the computed clusters.

## Is K-means ++ deterministic?

k-means is deterministic except for initialization. You can initialize with the first k objects, then it is deterministic, too.

## Does Kmeans depend on initialization?

When the data has overlapping clusters, k-means can improve the results of the initialization technique. When the data has well separated clusters, the performance of k-means depends completely on the goodness of the initialization.

## Does K mean capitalized?

Neither means nor clustering is a proper noun, so they should be capitalized only in a title (if such capitalization is customary in the context; in Wikipedia it is not).