Learn Data Science with K-Means Clustering

Data Science is becoming an important factor in every business. The data has been collected, analyzed, and interpreted with the help of data science. It has many implications in today’s world. The data science cycle is very helpful for many businesses. In the financial sector, the data science cycle is playing an important role. Data science uses machine learning models and Artificial Intelligence models to predict the future. These models predict the future by learning past data. In the financial sector, profit or loss can be predicted using the help of data science.

Clustering means a learning method. In the clustering method, the population is divided into clusters or several groups. It can be defined as the process of grouping a population or set of data points into groups so that data points in the same group are more similar to the data points in another group than data points in different groups.

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What is K-Means Clustering?

As we discussed earlier clustering is the process of grouping a population into groups. It has many implications in data science as well as other fields. Programming languages also make use of the clustering process. K means clustering is a type of clustering method. K means clustering can be explained as the independent machine learning algorithm which performs the clustering task. In this type of clustering, the n mentions are grouped into K clusters. This grouping is based on distance. Within the cluster, variance is somewhat minimized using this cluster.

Let us look at how the K Means algorithm works. In the K Means algorithm, it is needed to specify the K.Randomly initializes the number of K centroids for a particular value of K and divides the data points into K clusters. Calculates the distance between each input to the K centroid and reassigns it to the cluster with the minimum distance after the re-task, replace the centroid of every cluster via way of means of calculating the suggest of the statistics factors withinside the cluster, repeat steps 2 & 3 till there may be no re-task required.

Distance Metrix

In machine learning there are mainly four types of distance metrics are used. They are:

1. Euclidean Distance
2. Manhattan Distance
3. Minkowski Distance
4. Hamming Distance

The most commonly used matrix distance in K-Means Clustering is Euclidean distance. Let us start with that

• Euclidean Distance

The shortest distance between two points is represented by Euclidean distance. It is a very common distance metrix used in machine learning, especially in K Means clustering. Euclidean distance deals with two dimensions. The number of dimensions is represented as n and p1 and q1 are the two data points in the euclidean distance.

• Manhattan Distance

The sum of absolute differences between points across all the dimensions is termed manhattan distance. It is also comprehended as city block distance.

• Minkowski Distance

It is termed the generalized form of both Euclidean distance and Manhattan distance.

• Hamming Distance

The similarity between two strings of the same length is measured by Hamming distance. This only works in the condition that the strings in the arrays have the same length.

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Applications of K Means Clustering

Clustering helps every machine learning engineer find out accurate results for the algorithm. The aim is to find out solutions for business as well as real-life problems. The application of K Means clustering is widely spread in different fields. Fields such as :

• Academic Performance
• Diagnostic Systems
• Search Engines
• Wireless Sensor networks

In academic performance, the marks of students are obtained and are used to categorize grades like A, B, C, etc. In the medical profession, diagnostic systems are used. It uses K-means clustering to get smarter medical support that too mainly in the treatment of liver ailments. Search engines are commonly used nowadays. Clustering is considered the backbone of search engines. When we search for a particular topic, the search must be grouped and for this purpose, clustering helps search engines. In wireless sensor networks, the algorithm of clustering plays a key role in finding the cluster heads. These cluster heads collect all data in their respective cluster.

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Conclusion

Clustering can be said as a key factor in data science and machine learning. It is used to find out the accurate results from algorithms. K Means clustering which supports various kinds of distance measures and gives accurate results to machine learning engineers.

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