**Clustering algorithm** (cluster analysis) is a grouping method of objects based on their similarities. It is used as a data analysis technique to discover interesting patterns in data, such as groups of customers based on their behavior.

Clustering, which is one of the unsupervised algorithms, can be used in many fields, including image processing, machine learning, graphic, pattern recognition, information retrieval analysis, biformatics, and compression.

Since Clustering is a complex concept, there are many algorithms for clustering and therefore there are several cluster models. The clusters produced by two different clustering algorithms will definitely be different from each other.

**Clustering **has different algorithms, each of which has its own characteristics and different approaches. Among the clustering algorithms, we can mention DBSCAN, cumulative hierarchical clustering, BIRCH, Gaussian mixture model, K-Means and Mean-Shift. In the following, we introduce these algorithms.

The DBSCAN algorithm stands for Density-Based Spatial Clustering of Applications with Noise and means spatial clustering based on the density of applications with noise. DBSCAN clustering is an extremely useful clustering algorithm for unsupervised learning problems.

DBSCAN is a density-based **clustering algorithm** that works on the assumption that clusters are dense regions in space separated by less dense regions.

The most exciting feature of the DBSCAN algorithm is that it is robust to outliers. There is also no need to specify the number of clusters in advance, unlike K-Means where we need to specify the number of centers.

K-Means Clustering is one of the simplest and most popular **clustering algorithms** that groups the unlabeled data set into different clusters. In this algorithm K defines the predefined number of clusters to be created in the process, such that if K = 2 there will be two clusters, for K = 3 there will be three clusters and so on.

This is an iterative process of assigning each data point to groups, slowly clustering the data points based on similar characteristics. The goal is to minimize the sum of the distances between the data points and the cluster center to identify the correct group to which each data point should belong.

K-Means is a centroid-based algorithm, where each cluster is associated with a centroid. Each cluster has data points with some commonalities and is far from other clusters. The main goal of this algorithm is to minimize the sum of the distances between data points and their corresponding clusters.

The algorithm takes the unlabeled data set as input, divides the data set into k-number of clusters, and repeats this process until it finds the best clusters. The value of k must be predetermined in this algorithm.

Agglomerative Hierarchical **Clustering **is a popular example of HCA. This algorithm follows a bottom-up approach for grouping data sets into clusters.

This means that this algorithm considers each data set as a single cluster at first and then starts combining the closest pairs of clusters together. It does this until all the clusters are merged into a single cluster that contains all the data sets. This hierarchy of clusters is shown in the form of a dendrogram.

**Gaussian Mixture Models** (GMM) is not a model, it is a probability distribution. It is a universal model used for unsupervised learning or generative clustering. GMM is also called expectation-maximization clustering or EM **clustering** and is based on an optimization strategy.

Gaussian mixture models are used to represent normally distributed subpopulations within an overall population. The advantage of mixture models is that they do not require which subpopulation a data point belongs to. Allows the model to learn subpopulations automatically.

A Gaussian mixture model (GMM) attempts to find a combination of multidimensional Gaussian probability distributions that best models each input data set.

Mean-Shift clustering is an unsupervised machine learning **clustering algorithm** used to identify clusters in a dataset. It is widely used in real-world data analysis (eg, image segmentation) because it is nonparametric and does not require any predefined shape of clusters in the feature space.

Mean-Shift is a density-based clustering method that focuses on finding regions of high density and repeatedly shifting data points toward the highest density of points, hence the name “shift mean.” Unlike the K-Means algorithm, this algorithm does not require any prior information about the number of clusters in the data, and is particularly useful for exploratory data analysis.

K-means clustering algorithm and cumulative **clustering **are among the most common clustering algorithms. Since these algorithms do not adequately address the problems of processing large datasets with limited resources (such as memory and cpu), the BIRCH algorithm comes into play. This algorithm is very useful because it performs accurate clustering on large data sets and is easy to implement.

(BIRCH) is a **clustering algorithm** that can cluster large data sets by producing a small and compact summary of the large data set that retains as much information as possible. This smaller summary is then clustered.

BIRCH is often used to complement other clustering algorithms by creating a summary of the dataset that the other clustering algorithm can now use.

C**lustering algorithm** has many applications. Three of them will be discussed in the following:

**clustering **the text and grouping it into different categories. Unsupervised techniques such as LDA are also useful in these cases to find hidden topics in a large dataset.

The ability to monitor the progress of students’ academic performance has been a critical issue for the higher education academic community. **Clustering algorithm** can be used to monitor students’ academic performance.

Based on students’ scores, they are grouped into different clusters (using k-means, fuzzy c-means, etc.), where each cluster represents a different level of performance. By knowing the number of students in each cluster, we can know the average performance of a class as a whole.

Reference: Part of the content of this article is taken from medium, javatpoint and analyticssteps websites.

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