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**forgy: Initialization of cluster prototypes using Forgy’s algorithm**

- Description Initializes the cluster prototypes using the centers that are calculated with Forgy’s algorithm (Forgy, 1965), which is the earliest algorithm for seeding the clusters in the standard K-means clustering.
- Usage forgy (x, k)

- Arguments x a numeric vector, data frame or matrix. k …
- Value an object of class ‘inaparc’, which is a list consists of the following items: v …

- Details …
- References …

- Examples

Contents

- 1 K-means and Genetic Algorithm for Clustering
- 2 What is Forgy’s algorithm?
- 3 Are there any optimizations of Lloyd-Forgy’s k-means clustering?
- 4 What is the difference between Lloyd’s and MacQueen’s clustering algorithm?
- 5 What is the Forgy method in k-means?
- 6 Information related to the topic forgy’s’ algorithm for clustering

## K-means and Genetic Algorithm for Clustering

## What is Forgy’s algorithm?

Forgy’s algorithm involves iteratively updating k k seed points which, at each pass of the algorithm, define a partitioning of the data by associating each data point with its nearest seed point. The seeds are then updated to represent the centroids (means) of the resulting clusters and the process is repeated.

## Are there any optimizations of Lloyd-Forgy’s k-means clustering?

There are at least two known optimizations of the original Lloyd-Forgy’s K-Means clustering, such as the Fuzzy C-Means and K-Means++ algorithms, discussed in [2,3,6].

Are there any optimizations of Lloyd-Forgy’s k-means clustering?

There are at least two known optimizations of the original Lloyd-Forgy’s K-Means clustering, such as the Fuzzy C-Means and K-Means++ algorithms, discussed in [2,3,6].

What is Lloyd’s k-means algorithm?

The k -means clustering algorithm, a staple of data mining and unsupervised learning, is popular because it is simple to implement, fast, easily parallelized, and offers intuitive results. Lloyd’s algorithm is the standard batch, hill-climbing approach for minimizing the k -means optimization criterion.

What is Lloyd-Forgy’s k-means procedure?

The classical Lloyd-Forgy’s K-Means procedure is a basis for several clustering algorithms, including K-Means++, K-Medoids, Fuzzy C-Means, etc. Although, some of these algorithms cannot be effectively used for clustering, due to the potentially huge computational complexity.

What is Lloyd’s k-means?

Algorithm 1 Lloyd’s k -means algorithm—the standard algorithm for minimizing J ( X , C ). Like other algorithms presented in this chapter, this algorithm’s pseudocode is presented simply, without details on efficiency optimizations used in real implementations

## What is the difference between Lloyd’s and MacQueen’s clustering algorithm?

Like MacQueen’s algorithm (MacQueen, 1967), it updates the centroids any time a point is moved; it also makes clever (time-saving) choices in checking for the closest cluster. On the other hand Lloyd’s k-means algorithm is the ffirst and simplest of all these clustering algorithms.

**Lloyd’s k-means algorithm is the ffirst and simplest of all these clustering algorithms**.

What is the difference between MacQueen’s algorithm and Lloyd’s K-means algorithm?

Like MacQueen’s algorithm (MacQueen, 1967), it updates the centroids any time a point is moved; it also makes clever (time-saving) choices in checking for the closest cluster. On the other hand Lloyd’s k-means algorithm is the first and simplest of all these clustering algorithms.

What is Lloyd’s algorithm?

R provides Lloyd’s algorithm as an option to kmeans (); the default algorithm, by Hartigan and Wong (1979) is much smarter. Like MacQueen’s algorithm (MacQueen, 1967), it updates the centroids any time a point is moved; it also makes clever (time-saving) choices in checking for the closest cluster.

What are clusters in machine learning?

In this method, the clusters are created based upon the density of the data points which are represented in the data space. The regions that become dense due to the huge number of data points residing in that region are considered as clusters.

Is it possible to achieve clustering using one specific algorithm?

It is not a single specific algorithm, but it is a general method to solve a task. Therefore, it is possible to achieve clustering using various algorithms. The appropriate cluster algorithm and parameter settings depend on the individual data sets. It is not an automatic task, but it is an iterative process of discovery.

## What is the Forgy method in k-means?

This method is one of the faster initialization methods for k-Means. If we choose to have k clusters, the Forgy method chooses any k points from the data at random as the initial points. This method makes sense because the clusters detected through k-Means are more probable to be near the modes present in data.

**chooses any k points from the data at random as the initial points**. This method makes sense because the clusters detected through k-Means are more probable to be near the modes present in data.

References:

forgy : Initialization of cluster prototypes using Forgy’s …

kMeans: Initialization Strategies- kmeans++, Forgy, …

A fast algorithm for robust constrained clustering

K-Means: Lloyd,Forgy,MacQueen,Hartigan-Wong – Stack …

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Questions just answered:

What is the difference between MacQueen’s algorithm and Lloyd’s K-means algorithm?

What is Lloyd’s algorithm?

What are clusters in machine learning?

Is it possible to achieve clustering using one specific algorithm?

What is the difference between Lloyd’s and MacQueen’s clustering algorithm?

What is the Forgy method in k-means?

Are there any optimizations of Lloyd-Forgy’s k-means clustering?

What is Lloyd’s k-means algorithm?

What is Lloyd-Forgy’s k-means procedure?

What is Lloyd’s k-means?

Are there any optimizations of Lloyd-Forgy’s k-means clustering?

What is Forgy’s algorithm?

forgy’s’ algorithm for clustering

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