K Means Clustering Algorithm

Algorithms have a wide variety of uses for both human beings and machines. These uses often revolve around the analysis and interpretation of large amounts of data. They are sometimes used for predictive models that tell what happens with a new set of data over a period of time. In other situations, they are more complex and better at placing information into categories. Either way, algorithms can be incredibly helpful at processing information much faster than a large group of human beings ever could. K means clustering is an example of one of these sorting algorithms. With a set of observations and guidance for forming clusters, a k means algorithm can use computing power in order to reshape a user’s understanding of what may seem like a disparate, nonsensical set of data.

 

K Means Clustering Algorithm Definition

 

A K means clustering algorithm is an algorithm which purports to analyze a number of observations and sort them in a fast, systematic way. In order to fully understand the way that this algorithm works, one must define terms. The two common variables in this algorithm are k and n. N refers to the observations being studied. It is another term for the datasets which will be entered into the algorithm. K refers to the clusters that will eventually be crafted to help organize and make sense of a large information set. K means clustering is the process by which the clusters are formed with their resulting data set.

 

A k means clustering algorithm works by splitting up data sets into clusters by looking at their means. These means are sorted by their distance to each other. This distance is known as variance. Clusters are formed with the smallest possible variance between the means. There are different ways in which the algorithm picks and sorts its clusters. The Forgy method has the machine learning algorithm picking a random observation and testing variance from that point. Random partition assigns a cluster to a set of data and then builds all other clusters around that initial data set. Either way, the result is a clear set of categories which future information can be placed into depending on its mean.

 

Learning Approaches

 

There are two major approaches to k means clustering as a machine learning algorithm. One of these is supervised learning. Supervised k means clustering algorithms work along with an example set. The example set may be a series of pre-filled categories or a cluster containing certain values. A k means clustering algorithm will then run and attempt to sort data sets into clusters and categories. The machine then compares the results of this analysis to the example set and looks at what categories and data points it got right and wrong. It uses this information to change the weights of the algorithm and the ways in which it is sorting means and drawing categories. Tests occur again and again until the results are close enough to the original example set and fall under a predetermined margin of error.

 

Artificial neural networks can be used to process that data. In the case of artificial neural networks, each cluster or set of means may be a particular node on a network. Nodes may also represent variables in the functions used in the algorithm. Once the algorithm begins to work and form categories, the system evaluates where data ended up and how close that output is to the original example set. It may then change the makeup of the nodes or the weights of each node depending on performance. The result is a flexible architecture that can handle k means clustering just as well as it can handle any other algorithm.

 

Unsupervised learning works a bit differently than supervised learning. It still may use the same architecture including artificial neural networks. However, it does not have an example set as its goal. Unsupervised learning takes a set of guidelines and general information and processes it over a period of time. This process may include the categories established by k means clustering. Learning comes from how the system processes the eventual output of the algorithm. This form of learning can produce more varied and unexpected results than a machine learning algorithm set up to replicate a particular example set.

 

K Means Clustering Algorithm Uses

 

The K means clustering algorithm has a number of different uses. It can help in making sense of a massive amount of data that may have no obvious correlation. Data is useless if it is a simple group of numbers with no relation to each other. Categories help point at relationships and trends between numbers. K means clustering quickly places large amounts of data into categories and can continue to do so over time. This continued effectiveness means that the algorithm may be used in a system that receives frequent inputs. By sorting inputs that have not otherwise been analyzed by a human operator, the algorithm can tell the operator a considerable amount about previously unknown information.

 

Thoughts on K Means Clustering Algorithm

 

K means clustering is not the only algorithm that should be processed through machine learning. Machines need to be able to predict and bring information together on multiple variables. However, k means clustering can definitely be viewed as a tool. It is one of the most effective algorithms at bringing in data and sorting it into categories quickly. This algorithm can also be used in multiple forms of learning. Any proper data mining operation needs to have k means clustering as part of its mining arsenal today.

R-ALGO Engineering Big Data provides articles on Artificial Intelligence, Algorithms, Big Data, Data Science, and Machine Learning. Also, R-ALGO Engineering Big Data provides R Tutorials on how to implement Machine Learning Algorithms with provided datasets.

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