K Means Clustering

-

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. Typically, unsupervised algorithms make inferences from datasets using only input vectors without referring to known, or labelled outcomes.

A cluster refers to a collection of data points aggregated together because of certain similarities.

How the K-means algorithm works

To process the learning data, the K-means algorithm in data mining starts with the first group of randomly selected centroids, which are used as the beginning points for every cluster, and then performs iterative (repetitive) calculations to optimize the positions of the centroids

It halts creating and optimizing clusters when either:

  • The centroids have stabilized — there is no change in their values because the clustering has been successful.
  • The defined number of iterations has been achieved.


Comments

Post a Comment

Popular posts from this blog

Linear Regression with single variable

-
Image
Linear Regression Algorithm From Andrew Ng’s Machine learning course The above diagram conveniently gives a brief overview of the Linear Regression Algorithm. We feed our learning algorithm with some data set(training set), which then outputs a function called Hypothesis. Hypothesis approximates a target function for mapping inputs to outputs. For linear regression in one variable, our hypothesis function is of the form — h(x) = θ0 + θ1x where θ0 and θ1 are the parameters.
Read more

Introduction Deep Learning

-
Deep learning is a branch of  machine learning   which is completely based on   artificial neural networks , as the neural network is going to mimic the human brain so deep learning is also a kind of mimic of the human brain. In deep learning, we don’t need to explicitly program everything. The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture. A formal definition of deep learning is- neurons "Deep learning is a particular kind of machine learning that achieves great  power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones". In the human brain app...
Read more

Artificial Neural Network (ANN)

-
Image
The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence modeled after the brain. An Artificial neural network is usually a computational network based on biological neural networks that construct the structure of the human brain. Similar to a human brain has neurons interconnected to each other, artificial neural networks also have neurons that are linked to each other in various layers of the networks. These neurons are known as nodes. Artificial neural network tutorial covers all the aspects related to the artificial neural network. In this tutorial, we will discuss ANNs, Adaptive resonance theory, Kohonen self-organizing map, Building blocks, unsupervised learning, Genetic algorithm, etc. What is Artificial Neural Network? The term " Artificial Neural Network " is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one...
Read more