Clustering Algorithms in Machine Learning

Introduction

Clustering, or what we even call cluster analysis, is a machine learning method, that assembles or groups the unlabeled dataset. It can be described as “a way clustering or grouping data points into various clusters, that contain identical data points. The objects with similarities are collected in a group and these objects have very little or no similarities to the objects collected in another group. n other words, the aim of clustering is to separate groups with like traits and pack them together into clusters.

Clustering is an unsupervised learning method; thus, the algorithm does not have any kind of supervision and it deals with unlabeled datasets. Each group or cluster is given a specific cluster ID. Machine learning systems can use this id to ease the processing of complex and large datasets.

Why is Clustering Important

Clustering is important since it evaluates the intrinsic grouping amongst the unlabeled data that is available. The criteria for clustering depends on the user, i.e. what kind of clustering they prefer based on their requirements. For example, one may want to identify the representatives for homogeneous groups (data reduction), or to locate “natural clusters” and define their hidden properties (“natural” data types), or identify suitable and useful groupings (“useful” data classes). Segregating data into clusters helps to understand the data’s underlying structure and finds applications across industries. For example, clustering can help classify diseases in the field of medical science and can be useful in customer classification in marketing research.

In some applications, data partitioning can be the final goal. On the other hand, clustering is a prerequisite while for preparing for other machine learning or artificial intelligence problems.

Kinds of Clustering Algorithms/Problems

Given the individual nature of the clustering tasks, there are different algorithms that are suitable for different types of clustering problems. Following are a few types of Clustering Algorithms:

• Connectivity Models: Connectivity models classify data points depending on the closeness of data points. This model is based on the idea that data points that are closer to each other portray more identical characteristics than those placed farther away.
• Distribution Models: Distribution models depend on the possibility of all data points in a cluster fitting into the same distribution, which is the Gaussian distribution or Normal distribution. One disadvantage of this model is that it is highly prone to overfitting.
• Density Models: Density models search for the data space for diverse densities of data points and separate the various density regions. It then allocates the data points within the same region as clusters.
• Centroid Models: Centroid models are reiterative clustering algorithms where the similarity depends on the algorithm’s closeness to the cluster’s centroid (the center of the cluster). The centroid ensures that the distance of the data points is minimal from the center. An example of centroid models is the K-means algorithm.

Applications of Clustering

Clustering has different applications across industries and is an effective solution to many machine learning problems. Following are a few applications of clustering:

• Market research to discover and characterize suitable audiences and audience bases.
• Classify diverse species of animals and plants with the help of image recognition techniques
• Derive animal and plant taxonomies and classifies genes with similar functionalities to gain an insight into structures inherent to populations.
• Identify areas of similar land use and classifying them as commercial, agricultural, industrial, residential, and so on.
• Classifies documents on the web for information discovery.
• Social media also use clustering techniques to classify all posts with the same hashtag under one stream.

Conclusion

Clustering is an essential part of machine learning and data mining. It segregates the datasets into groups that have similar characteristics, that help with improved user behavior predict.