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Complete Tutorial on Clustering in Data Mining for Beginners and Experts

Complete Tutorial on Clustering in Data Mining for Beginners and Experts

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 mining

Clustering 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 the types of clustering in data mining methods, it would be helpful to be aware of its applications. As you read about the various methods, you can understand why one method works better for an application when compared to others. Here are the most well-known applications of clustering in data mining.

1. Business & Marketing

Perhaps the area that uses clustering in data mining techniques the most is marketing. Clustering is used to categorize the shoppers on the basis of their preferences. Businesses can create clusters of their customers and try to understand the behavior of each group. It helps in creating advertising strategies that target each group to an optimum level. Clustering is also behind the smooth running of recommendation engines.

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

Online financial transactions have become the standard in recent times. The increase in online transactions has also been accompanied by a similar increase in credit card frauds. Data mining enables financial institutions to swiftly sift through all the data and detect anomalies or outliers. The institutions can then warn the account holder regarding the same or take appropriate actions.

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 K-means clustering. K-means clustering is a classic example of a partition-based clustering method. Apart from K-means, K-medoids, CLARA, and CLARANS are some of the other types of partition-based clustering in data mining algorithms.

what is clustering in data miningThese algorithms are iterative. After creating random initial partitions, the points are again analyzed based on a metric and then reassigned to another partition if needed. The process is repeated until you obtain partitions where items in a partition are most similar to each other.

Partitioning techniques require you to predefine the number of partitions. It is one of the principal drawbacks of this method. Another major limitation of this method is that it has to classify every data point into a cluster. Sometimes, points that should be outliers are forcefully assigned to a cluster, and you miss out on gaining some key insights from these outliers. These methods are more focussed on the cluster centers, and as a result, the border points get very little emphasis.

2. 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 techniques
Once the algorithm has finished running, all the points are either core points, boundary points or outliers. The core and boundary points are parts of clusters, whereas the outliers are the ones that do not belong to any cluster.

Density-based clustering in data mining methods can help in identifying financial frauds or anomalies. You can examine the outliers closely and investigate their cause. The only drawback to these clustering in data mining methods is that it has trouble classifying points when there are overlapping Gaussian distributions involved.

3. 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 mining
In the divisive approach, the whole data set is considered to be a single cluster which is then divided into subgroups based on some distance metric.

The key drawback of hierarchical clustering in data mining techniques is that they are computationally heavy and require significant memory as well as resources.

4. 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.

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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 miningFor each point, the probability of the data point belonging to a particular distribution is calculated. The ones that have the highest probability become part of the cluster, whereas the ones whose probability is below a certain threshold are not included in the cluster.

They may be a part of another cluster or maybe labeled outliers.

The major drawback of this method is mainly that the data can become overfitted at times. You have to achieve the perfect balance between complexity and practicality. An overly complex model can provide valuable insights into the data. However, it can also lead to overfitting. The complexity should be limited to a level so that the model provides the required insights without overfitting data. The expectation-maximization algorithm is a classic example of this type of clustering in data mining.

Learn 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 study thoroughly. Its widespread application makes it an invaluable tool for all data science jobs.

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The demand for data science is huge and expected to grow exponentially. Taking the right steps given here can help you build a career in data science and ensure better prospects. Enroll in Digital Vidya’s Data Science Master Course to create a strong foundation in Data Science & build a successful career as a Data Scientist.

We hope that you find this blog useful and beneficial & helped you to find answers to specific queries related to pursuing a career in data science. We wish you success and good luck!

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