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What is Clustering Algorithm in Machine Learning?

Nilesh Parashar
What is Clustering Algorithm in Machine Learning?

A clustering algorithm is a form of Machine learning technique that may be used to separate data sets depending on groupings and business requirements. It is a prominent type of Machine Learning algorithm used in data science and artificial intelligence (AI). Based on the logical grouping pattern, there are 2 types of clustering algorithms: hard clustering and soft clustering.


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K-Means clustering, connection models, centroid models, distribution models, density models, and hierarchical clustering are some of the common clustering approaches based on Clustering techniques that have applications in picture segmentation, market segmentation, and social network analysis.


Clustering Method Types

The clustering methods are widely split into Hard clustering (datapoint belongs to just one group) and Soft Clustering (data points might belong to another group as well) (data points can belong to another group also). However, there are different ways of Clustering that exist. The following are the most common clustering approaches used in machine learning:

  • Partitioning Clustering
  • Density-Based Clustering
  • Clustering Based on Distribution Models
  • Clustering by Hierarchy
  • Fuzzy Clustering

Clustering Partitioning

It is a sort of clustering in which data is divided into non-hierarchical groupings. The most common example of this method is the Agglomerative Hierarchical algorithm. The K-Means Clustering technique is the most prominent example of partitioning clustering.

The dataset is partitioned into a set of k groups in this manner, where K is the amount of pre-defined the cluster center is designed in such a manner that the space between the data points of one cluster and the centroid of another cluster is as short

Clustering Based on Density

The density-based clustering approach groups dense regions into clusters and arbitrarily shaped distributions are generated as far as the dense region can be linked. This program accomplishes this by detecting distinct clusters in the dataset and connecting high-density areas into clusters. Sparser regions separate the dense areas in data space.

Clustering Based on Distribution Models

The distribution model-based clustering approach divides data based on the chance that a dataset corresponds to a specific distribution. The grouping is accomplished by assuming specific distributions, most notably the Gaussian Distribution.

An example of this kind is the Expectation-Maximization Clustering technique that employs Gaussian Mixture Models (GMM) (GMM).

Clustering by Hierarchy

Hierarchical clustering is used as an alternative for partitioned clustering because there is no necessity to pre-specifying the number of clusters to be produced. The dataset is separated into clusters in this approach to form a tree-like structure known as a dendrogram. By pruning the tree at the appropriate level, the data, or any cluster centers may be picked. The Agglomerative Hierarchical algorithm is the most typical example of this strategy.


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Fuzzy Clustering

Fuzzy clustering is a soft approach in which a data object can be assigned to more than one group or cluster. Each dataset has a set of membership coefficients that are proportional to the degree of membership in a cluster. The fuzzy C-means method, also known as the Fuzzy k-means algorithm, is an example of this sort of clustering.

Algorithms for Clustering

The Clustering methods may be separated depending on the models that are mentioned previously. There are various types of clustering algorithms published, however, only a few are regularly utilized. The clustering technique is dependent on the sort of data model that we are utilizing. Some algorithms, for example, must use prediction to the number of clusters in the supplied dataset, whilst others must discover the shortest distance between the datasets.

K-Means Algorithm

The k-means method is among the most widely used clustering techniques. It classifies the dataset by separating the samples into equal variance groups. This approach requires the number of clusters to be provided. It is quick with fewer calculations necessary, having the linear complexity of O(n) (n).

Mean-Shift Algorithm

The mean-shift method seeks dense places in a uniform density of data points. It is an instance of a centroid-based model, which works on updating the centroid candidates to be the center of the points inside a specified region.


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Nilesh Parashar
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