There is a mind-boggling amount of data that is being generated every day. We generate over 295 billion emails, conduct 5 billion searches and send 65 billion Whatsapp messages every day. And, as a matter of fact, this number is rising steadily.

Data mining, true to its name, digs through all this data to uncover the hidden gold or in this case, hidden insights. While there are many techniques employed by data scientists towards this end, clustering in data mining is one of the most vital methods that you should know about.

From Amazon’s Alexa to the arrangement of products in your nearest supermarket, everything can benefit from data mining techniques. So what is clustering in data mining? Why is it needed? How to do clustering? Continue reading to get answers to these pertinent questions.

What is Clustering in Data Mining?

Data mining produces a tremendous number of data points. Analyzing each of them on their own will take up an enormous amount of time and resources. Many of these data points will be similar, and it would benefit you greatly to group these data points before running the analysis.

A cluster is essentially a group of objects that share some common characteristics. The process of creating such clusters is known as clustering in data mining. It identifies those objects that are similar and forms clusters. Every cluster will have elements that are more similar to each other than the ones in other clusters.

clustering in data miningClustering in data mining

Classification and clustering are two data mining processes that are often confused with each other. Both of them involve grouping data points into groups. However, the difference is that while classification is a supervised learning technique, clustering in data mining is an unsupervised learning technique that creates groups without any prior training.

The clusters are not labeled beforehand in clustering. Another key difference is that while classification uses the knowledge from training sets to create the classes, clustering in data mining techniques has no prior training.

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What are the Applications of Clustering in Data Mining?

Before reading about the1. Business & Marketing

2. Biology

The field of biology uses clustering in data mining in a wide range of applications such as gene typing, transcriptomics, sequence analysis and the study of plant and animal ecology. Inferring the population structures is another application of clustering in data mining techniques in biology.

3. Medicine

Various types of clustering in data mining techniques are employed to enable the correct identification and classification of cancerous and abnormal tissue by analyzing the scan. It reduces human error in diagnosing a condition and also adds an extra level of assurance to the final diagnosis. It is also used in studying antimicrobial activity and in demarcating the regions for radiation therapy in cancer patients.

4. Image Processing

You may have noticed how some cameras automatically detect faces or objects. It is achieved by clustering the pixels and identifying the borders and objects.

5. Fraud Detection

6. Politics

Many research institutions conduct polls to gauge people’s opinions about various topics. Employing clustering on the data enables politicians to gain a better understanding of the people in the area. He/she can then align their campaign in a manner that aims to get them the maximum votes.

Clustering in Data Mining Methods

1. Partitioning-Based Method

Partitioning based methods divide the data set into a finite number of clusters or partitions such that each cluster contains at least one item and each item can belong to only one partition. Suppose you have n items and m partitions. These n items will be distributed into these m clusters such that if we add up the number of items in each cluster, the result should be n.

When you start exploring what clustering in data mining is and what are some of the types of clustering in data mining algorithms, then one of the first ones you would encounter is the what is clustering in data mining2. Density-Based Method

Unlike partition-based methods, density-based clustering is more intuitive. The number of clusters is not fixed from the start. These algorithms work by identifying natural clusters in the data by analyzing the density around the data sets.

DBSCAN and OPTICS are density-based clustering algorithms that most commonly in use. The size or shape of each cluster is not defined and can be arbitrary.

These algorithms sift through each point and look at its neighborhood to determine if the point can be the part of a cluster. Once such a point has been identified, the algorithm immediately adds the points closest to it to its clusters. The process is again repeated for the newly added points, thereby increasing the cluster size.

clustering in data mining techniques3. Hierarchical Method

True to its name, hierarchical methods establish a hierarchy among the data points. Connectivity methods is another name for hierarchy methods. There are two distinct methods to establish the hierarchy – agglomerative and divisive. The agglomerative method is the one that is used most often in the real-world. The divisive method is mostly used theoretically, and its real-world applications are very limited.

In the agglomerative approach, each point in the data set sets out as a separate cluster. The distances between these clusters are measured, and the closest ones are identified and merged into a single cluster. The process gets repeated until you are left with a single cluster or until you have uncovered a required number of clusters.

clustering in data mining4. Grid-Based Method

Grid-based models clustering in data mining is very similar to density-based models. The data space is quantized into a grid. The cells that are closer together form clusters. Grid-based techniques are very particularly useful when the data that needs to be classified is non-numeric.

This is not to say that they aren’t suited for numeric data. But grid-based models provide results that surpass the ones from other types of clustering in data mining techniques when it comes to non-numeric data types. These methods are also incredibly fast and offer a lot of flexibility.

STING and CLIQUE are the two most used grid-based clustering in data mining techniques. Both of them work along similar lines. The data space is divided into a grid that consists of various cells. The algorithm computes the density of each cell, and if it is high enough, the cell becomes a cluster. Other cells neighboring this cell can also become a part of the cluster if their density is higher than the threshold. The process gets repeated until all the cells are covered.

The grid-based and density-based methods differ only in the way the neighborhood is calculated. Since the grid-based clustering in data mining uses cells, the clustering happens faster and consumes fewer resources.

5. Model-Based Method

The model-based method is also known as a distribution-based method. The underlying assumption is that every cluster consists of data points from the same distribution. The distributions under consideration are mostly normal or Gaussian distributions since most real-world data does originate from Gaussian distributions.

clustering in data miningLearn Clustering in Data Mining to Further Your Career

You can clearly see how valuable clustering in data mining is and how widely it is used. You may have already encountered its application without even realizing the underlying technology that was in use.

If you would like to start a career in data science or data mining, then data mining is one of the topics that you should Conclusion

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