Clustering — An Unsupervised Machine Learning method
Clustering of data comes under unsupervised machine learning.
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Updated November 2026. This was first published on 9 November 2020. The idea is the same. Any number below is what the original piece said then. It is not a new count.
Key takeaways
- Clustering of data comes under unsupervised machine learning.
- Here Clustering of data means creating a subset or grouping of similar behavior of data.
- For Business aspect, it is very important to have a cluster for customer so that we can provide similar service or offer a similar product to the customer who shows similar behavior or have similar needs.
Clustering of data comes under unsupervised machine learning. Here Clustering of data means creating a subset or grouping of similar behavior of data. For Business aspect, it is very important to have a cluster for customer so that we can provide similar service or offer a similar product to the customer who shows similar behavior or have similar needs.
The clustering of the customer can help different businesses in servicing their customer in a better way and improve customer satisfaction. Let’s discuss some advantages of the cluster in the different business domains.
E-Commerce companies such as Amazon or the entertainment industry such as Netflix can create clusters based on customer profile, customer satisfaction, customer preferences, past buying information, demographics, and customer search behaviors. We can analyze these clusters to tailor products and services and deliver personalized offers that can improve customer satisfaction and retention.
Improve customer experience ?** Especially to the service industry such as restaurants, Hotels, and Motels industry or self-service sectors such as Online Banking, tours and travels booking agencies, and sports clubs customer experience is very important. The cluster can help us to improve operational efficiency and a customer can get a better experience and that drives to more sales.
Banking Financial Services and Insurance (BFSI) and Retail industry can use customer cluster analysis to develop insights for cross-selling and up-sell opportunities which can be initiated at right time for each customer. Identifying which customer needs health insurance for family, who need a credit card and who need a home loan all such decision can be easily made by using clustering insights.
Cluster can also help us in automating business processes such as customer credit risk evaluation process, customer complaint resolutions, first-level inquiry responses, and many other processes. These Business processes are repeated and very important for companies in making a decision related to offering services.
These are only a few advantages from the clusters we have discussed. When your business is growing at large or you are scaling your services you need clustering insights for better service, more customer satisfaction, and customer retention.
Kmeans clustering method is the most common and first choice for clustering. But there are other methods also which are more effective and gives better result compare to Kmeans. Let’s discuss clustering methods in detail with their advantages and disadvantages.
Different Types of Clustering Methods are as follows.
Hierarchical Clustering — Generally, we use Hierarchical Clustering for small datasets bec #
There are two different strategies for Hierarchical Clustering.
In this method, each observation starts as its cluster and pairs of clusters are merged as one moves up in the hierarchy.
In this method, all observation lies within one cluster and split performed recursively as one moves down in the hierarchy.
One major drawback of this method is it is very slow even for medium size datasets. In many cases, due to large memory overhead, it is not practically used by us.
K-Means Clustering — K-means Clustering is the most commonly used method for clustering. I #
Let understand How K-Means cluster works. Here I am giving you steps involved in K-Means cluster.
Define the number of groups that you want to create for the given dataset.
Randomly pick K observation from the given dataset as a centroid.
Data points are assigned to the closest centroid and form a cluster.
Recalculate centroid value and reassign another data point.
This process goes iteratively until all data points assigned to some cluster.
Kmeans clustering works very well for circular data like we are seeing in the above diagram. But in the case of linear data, it fails to create clusters accurately.
Kmeans can be used in many cases such as digit reorganization, compressing color in an image, financial fraud detection, etc.
GMM Clustering — Gaussian Mixture Model(GMM) Clustering is a probabilistic model and it as #
GMM implements expectation-maximization methods for fitting a mixture of Gaussian models. It computes the Bayesian Information Criterion to assess the number of clusters in the given data.
It is more flexible in terms of cluster covariance.
It accommodates a mixed membership model means each data point belongs to each cluster based on the probability of each data point which is calculated from cluster normal distribution.
GMM can be used for handwriting detection, fuzzy image segmentation, and assessing projectile accuracy.
Spectral Clustering — Spectral Clustering is another method of clustering. Spectral Cluste #
Spectral Clustering is graph-based clustering and it can be solved by standard linear algebra. Spectral Clustering uses the Similarity graph, Laplacian Matrix to create a K cluster for a given dataset. Spectral Clustering can be performed in the following steps.
Create a similarity graph between x1,x2,?..xN data point.
Calculate K eigenvector of its Laplacian Matrix to define features vector of each data point.
Now use K-Means on these feature vectors to get K clusters.
Here I have used many new words which are very difficult to understand. I try to explain these in simple ways for you. A similarity graph is used to define the relationship between data points based on local information.
A graph G=G(V, E) consists of a set of vertices and edges E. The Laplacian Matrix is defined as a graph L=D-A. where D called a degree matrix, is a diagonal matrix with jth diagonal elements. A is an adjacency matrix of graph G with n vertices is defined as n x n symmetric matrix with components aij =e or 0 if there is no connecting edge, where the diagonal elements aij are equal to 0 for all j=1,2,?
Spectral Clustering does not make a strong assumption related to cluster formed, unlike Kmeans which assume clusters are spherical. So in cases where data are spherical spectral clustering gives more accurate cluster result compare to Kmeans.
It is fast in computing clustering for sparse data set with several thousand elements. (sparse data means data which has missing values).
It is computationally expensive for large sample data due to the computation of eigenvalues and eigenvector. It also increases time complexity for large data.
MiniBatch Kmeans Clustering — MiniBatch Kmeans Clustering is used mostly for large sample #
Creating a Random sample and performing clustering is done sequentially. On each iteration, a new sample is created and used to update the clusters. Convergence is detected when no change in cluster occurs in several iterations. Each mini-batch updates clusters using a learning rate. This learning rate I defined as the inverse of the number of data assigned to a cluster during the process and its values reduce with the increasing number of iterations. As the number of iteration increases the effect of new data is reduced.
It takes less computational time to compare to K-Means Clustering.
It is very effective for large sample size data with a small number of clusters.
It gives an equivalent result similar to Kmeans for large sample with a large number of clusters(more than 20 clusters).
Mean Shift Clustering — Mean Shift Clustering is based on the concept of Kernel Density Es #
There is two Kernel function associated with Mean Shift Algorithm Flat Kernel and Gaussian Kernel function. We will get a different result based on the Kernel function used in Mean Shift function.
Let’s understand this algorithm with the following steps.
Each data point will be assigned to the cluster of its own.
Compute centroid by combining data points which are close to each other.
Iterate above steps and move to the higher density region.
Stop when centroid each at the position from where it cannot further.
Mean Shift does not depend upon the shape of the data point and the number of clusters, unlike K-Means clustering. The number of clusters will be determined by the algorithm on its own based on the dataset.
Mean Shift clustering is used in computer vision where it will be used for image segmentation. You can also use Mean Shift in Video tracking.
Advantages of Mean Shift Clustering are as follows.
You don’t have to specify the number of clusters to be created. This clustering Algorithm itself calculates the number of clusters.
It has only one parameter Bandwidth which decides the number of clusters and makes it more effective.
The disadvantage of Mean Shift Clustering are as follows.
It is very slow compare to K-Means Clustering.
It is very expensive and does not work well when you have high dimension data.
Variable Bayesian Gaussian Mixture Model (VBGMM) uses a variational inference algorithm and it is a variant of GMM clustering. VBGMM and EMGMM are two special cases of GMM methods.
VBGMM is an iterative algorithm that alternates between the probabilities for each data point to be generated by each mixture and fitting the mixtures to these assigned points. It incorporates prior information while creating clustering which avoids singularities. VBGMM bias all means towards the origin and covariance between the data points. The variational algorithm uses more hyper-parameters due to its Bayesian nature so VBGMM has many hyper-parameters. Alpha is the most important parameter of VBGMM. A high value of alpha leads to more uniformly-sized mixture components where as a small value leads to some mixture components getting almost all the data points while most mixture components will be centered on just a few of the remaining data points.
VBGMM is used for the detection of emerging faults for industrial systems, image segmentation, and many other cases.
you can find some relevant python code to perform clustering on below Github link.
Questions and answers #
What is the main point? #
Clustering of data comes under unsupervised machine learning.
What else did the original piece say? #
Here Clustering of data means creating a subset or grouping of similar behavior of data.