Top 5 Clustering Algorithms in Machine Learning
There are Yottabytes of sensitive data being generated from the interfacing of humans with machines. For cost-effective and optimal enrichment of this data, Machine Learning (ML) algorithms are our best bet. One of the most reliable categories of ML algorithms is clustering algorithms, irrespective of the complexity of data.
There are three different machine learning frameworks, classified based on the data you are working with — supervised learning, semi-supervised learning, and unsupervised learning. In supervised learning, the machine is trained with labeled data. The correct input and output data are provided to the machine.
Unsupervised learning works with data that is completely unlabeled, so it is left to the algorithm to find hidden patterns in the data. Semi-supervised learning is a combination of both the above machine learning approaches.
What are Clustering Algorithms and When are They Used?
Clustering algorithms are unsupervised learning algorithms that find as many groupings in the unlabeled data as they can. These groupings are referred to as ‘clusters’. Each cluster consists of a group of data points that are similar to each other based on their relation to surrounding data points.
Analysis that makes use of clustering algorithms may be especially pivotal when working with data that you know nothing about. Clustering algorithms are usually used when there are outliers to be found in the data or to do anomaly detection like in the case of the engineering consulting firm, Mechademy .
It is sometimes challenging to find the most suitable clustering algorithm for your data, but finding it will bring you indispensable insights into that data. Insurance fraud detection, categorization in libraries and warehouses, and customer segmentation are some real-world applications of clustering.
What are the Main Clustering Algorithm Models Based on Data Distribution?
As we have understood the fundamentals of clustering algorithms, we can get into their primary models or categories. These classifications arise based on the patterns that the data points need to be arranged in. The various clustering models are listed below:
The clustering algorithms built on the density model search for areas of varying density of data points in the data space. Data is then grouped by areas of high concentration of data points surrounded by areas of low concentration of data points. The clusters can be of any shape and there are no other constraints or data space outliers.
Under the distribution model, data is fit together based on the probability of how it may belong to the same distribution. In a particular distribution, a center point is determined, following which as the distance of a data point from the center increases, its probability of being in that cluster decreases.
This model consists of clustering algorithms wherein the clusters are formed by the proximity of the data points to the cluster center or centroid. The centroid is formed in such a way that the data points at the least possible distance from the center. Data points are differentiated based on multiple centroids in the data.
Hierarchical, or connectivity-based clustering, is a method of unsupervised machine learning that involves top-to-bottom and bottom-up hierarchies. These are implemented in hierarchical data from company databases and taxonomies. This model is more restrictive than the others, albeit efficient and perfect for specific kinds of data clusters.
Top 5 Clustering Algorithms
The foremost machine learning clustering algorithms are based on the above general models. The most fitting application of clustering algorithms would be for anomaly detection where you search for outliers in the data. Cluster analysis enables the discovery of patterns that you can use to find what stands out in the given data.
You can use clustering algorithms to solve other problems related to noise, interpretation, scalability, and so on. The most widely used clustering algorithms are as follows:
The most commonly used algorithm, K-means clustering, is a centroid-based algorithm. It is said to be the simplest unsupervised learning algorithm. Here, K defines the number of predefined clusters that need to be generated.
Each data cluster in the K-means algorithm is created in such a way that they are placed as far away as possible from each other. The data points in the clusters are allocated to the nearest centroid till no point is left without a centroid.
As long as the data has numerical or continuous entities, it can be analyzed with the K-means algorithm. In addition to being easy to understand, this algorithm is also much faster than other clustering algorithms.
Some drawbacks of this algorithm are incompatibility with non-linear data, outliers, and categorical data. Additionally, you need to select the number of data clusters beforehand.
This is a ‘sliding window’ type algorithm that helps to find areas with high densities of data points. The algorithm works by updating the candidates for the centroid to be the center of points within a given zone or region in the data.
The filtration of the candidate windows is done in the post-processing phase. The end result of this process is the formulation of a final set of centroids along with their allocated groups of data points.
The number of data clusters doesn’t need to be determined before the data analysis begins with the algorithm, unlike the K-means algorithm.
This algorithm, which stands for Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is similar to the Mean-shift. The DBSCAN algorithm separates the areas of high density from the low-density areas.
The clusters can then end up in any arbitrary shape. You will need a minimum number of points within the neighborhood of the starting data point to start the clustering process. Otherwise, those data points would end up as noise.
This algorithm can identify outliers like noise and arbitrarily shaped and sized clusters with much ease. The DBSCAN algorithm also does not require a pre-set number of clusters.
4)Expectation-Maximization Clustering using Gaussian Mixture Models
This algorithm is used usually for those cases where the K-means clustering algorithm fails. The naive use of the mean value for the cluster center is the main disadvantage of K-means. K-means fail when the clusters are not circular and when mean values of the data cluster are too close together.
In Gaussian Mixture Models (GMM), it is assumed that the data points are Gaussian distributed. Both the mean and the standard deviation are used as parameters to describe the shape of the cluster.
Each cluster mandatorily has a circular shape. Expectation-Maximization (EM) is an optimization algorithm that we use to find out the parameters of the Gaussian function. The cluster is then formed based on these parameters’ values.
5)Agglomerative Hierarchical Algorithm
The Agglomerative Hierarchical Algorithm performs the function of bottom-up hierarchical clustering on data clusters. When the algorithm starts with the data, each of the data points is treated as a single cluster.
With every succession, the algorithm merges the data points into a tree-like structure. The merging continues to occur until finally a single group with all the data points is created.
The clusters with the smallest average linkage are merged into one. You do not need to specify the number of clusters at the outset and can select the best-structured clusters.
Build Machine Learning Models for your Software Solutions
Nearly every business, established and emerging alike, is realizing the benefit of digitization with the latest technologies. The ease and efficiency of data analysis with machine learning algorithms is one such advantage.
Machine learning models and algorithms are the backbones of any Artificial Intelligence-based software application or modernization project. To begin the journey of modernizing your software solutions you can book a consultation with Daffodil’s AI experts .
Originally published at https://insights.daffodilsw.com.