Introduction:
Data science is leading the world. It is the combination of different techniques that are essential to perform different functions on data. Mathematics, statistics, artificial intelligence, and machine learning are integral parts of data science. Each part of data science holds different concepts of data science. Algorithms are one of them and when talking about clustering, some important clustering algorithms are essential to learning for becoming a data scientist. In this article, you will read the top clustering algorithms used in data science. So let’s get started. Are you looking to become a Data Scientist? Go through 360DigiTMG’s PG Diploma in Data Science and Artificial Intelligence!
Clustering is a technique of machine learning that includes grouping the data points. When talking about statistical analysis then clustering gains much importance. For classifying the data points into specific groups’ different clustering algorithms are used. Being a data scientist you need to be aware of the following top clustering algorithms.
K-means Clustering Algorithm
This is a very easy clustering algorithm and you can implement it without any difficulty. It does not require any computation that is why it is fast. It represents the critical side of the introductory data science graphically. But it can be more challenging for the k-means clustering algorithm to identify the groups in data. Due to its random choice of cluster center, it might be inconsistent. Become a Data Scientist with 360DigiTMG Data Science Training institute in Pune. Get trained by the alumni from IIT, IIM, and ISB.
Mean-Shift Clustering Algorithm
This is the second type of clustering algorithm that is used to find the dense areas in data science. As you have read that k-means randomly choose the cluster center but mean-shift eliminates that issue and works on the objective of locating the exact center of the cluster. You do not need to select the cluster by yourself. Also, check out this Data Science Course in Chennai with placement to start a career in Data Science.
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DBSCAN – Density-Based Spatial Clustering Algorithm
This clustering algorithm has reduced the problems found in the mean-shift clustering algorithm. You can start with a random data point and you are required to select the minimum points to start the cluster. So here you do not need to select a specific number of a cluster like mean-shift. It does not force the points to be a part of the cluster, instead, it takes them as a noise in the cluster. But for high dimension data, it might prove difficult to regulate the distance threshold. A career in data science by enrolling in the Data Science Certification in Bangalore offered by 360DigiTMG.Earn yourself a promising
EM using GMM – Expectation-Maximization (EM) Clustering using Gaussian Mixture Model (GMM)
These clustering algorithms are very flexible than the k-means clustering algorithms. And it starts with assumptions. The mean and the standard deviation are the parameters that predict the shape of the cluster. It will make an elliptical shape because it has a deviation in both axes. EM finds the parameters for every cluster. The overlapping clusters can be further defined by saying that it belongs to the first cluster or the other. It is similar to k-means and selects the number of clusters randomly.
Agglomerative Hierarchical Clustering
This consists of two sub-categories that are top-down and bottom-up. In the bottom-up approach, every data point is considered as a separate cluster from the initial level and then continues making pairs until you get a single cluster at the end. In the top-down clustering method, the start is from a single data point and then making possible groups from it. You are not required to tell the specific number of clusters and it is not sensitive in the context of choosing the distance.
Being a data scientist, you have to be well aware of these top clustering algorithms as they play an important role in data science.
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