Data Clustering is the most renowned methodology in unsupervised learning by which data is grouped with regards to the similarity of the data points. It has numerous real-life applications where it can be deployed in a variety of scenarios. Data clustering algorithms are widely used in different fields.The fundamental working principle behind clustering is assigning a big dataset into subgroups termed clusters in a specific manner that attributes in the same cluster share a degree of certainty. This methodology is inspired by the cognitive process of the human mind to distinguish objects based on their incongruity.For instance, when you go out shopping, you can easily differentiate between mangoes and watermelons, even if they are kept in the same space or container. You distinguish these two items based on their size, color, texture, and other sensory attributes that your brain perceives differently for both items. Clustering is an emulation of this cognitive process to make machines capable of differentiating among different items.[lwptoc skipHeadingLevel="h1,h4,h5,h6"]In unsupervised machine learning, this methodology is used to identify objects lacking external labels attached. The machine learns the features and patterns automatically on its own without any given input-output directives. The clustering algorithm can retrieve hidden attributes by intelligently examining datasets and then create different classes to group them appropriately.The algorithm divides the populated dataset into different groups. Each data point is congruent to the data points in the same group and contrasting to the data points in the other groups. Based on these similarities and dissimilarities, it then separately labels each sub-group known as a cluster.In this article, we have discussed 8 Applications of Data Clustering Algorithms.
Real-World Applications of Data Clustering Algorithms
Identifying Fake News
Fake news plays an increasingly dominant role in spreading misinformation by impacting people’s perceptions or thinking about a certain event to deform their awareness and decision-making. According to research, Fake news is being spread faster than the actual news. This spread rate is further accompanied by technological mediums such as social media. In a recent study at the University of California, computer science students use clustering algorithms to identify fake news based on its content.The said algorithm works in a way that by examining the content of the fake news blog posts, filtering certain words, and then clustering them. These dedicated cluster sets are utilized by the algorithm to separate fake content and authentic news. Usually, a specific set of words are used in hyped and click-bait blog posts. Upon detecting a high percentage of these certain words in content, the algorithm declares such material as fake or fiction.
You know about the spam folder in your email, where emails declared as spam by the algorithm are stored. Spam emails are one of the most annoying factors of today’s digital marketing tactics. The worst these spam emails can do is phishing for your sensitive information and confidential data. Email service providers use machine learning algorithms to perform clustering to avoid getting these emails in your primary inbox session. The main purpose of these algorithms is to identify an email as spam or not.K-Means approach is a highly effective clustering algorithm to identify spam emails. It works by assessing the different email sections such as subject, sender’s email, and body. The data is then grouped in other clusters based on its attributes, revealing the nature of email as spam or useful.Using clustering methodology in the classification process helps in attaining the accuracy of the filter around 98%. This high level of accuracy ensures that users won’t miss out on their desired emails, letters, or offers.
Targeted marketing is the backbone of digital marketing. This process is driven by evaluating consumers' behaviors and approaching them with dedicated campaigns or offers that they are likely to accept.If you are a business owner and struggling to get the high RoI, it is high time to target the right people in the right way. Approaching your customer contrary to the said strategy may damage your sales and lose your Customer’s interest.Clustering algorithms are capable enough to group consumers with similar buying habits and likelihood to purchase specific items. Once you have the groups, you can test campaigns on each group with different products to help you engage them more effectively for a similar campaign in the future.
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Let’s assume the case of a car manufacturer here. A car manufacturer wants to rectify and make intelligent guesses over the customer’s behavior and buying habits to evaluate which product is experiencing more sales and their customers’ mindset behind their buying habits.They use to process each customer’s details based on the available information to decide which product should be manufactured more and the attitude and interest of customers towards a certain product. This study helps the company monitor and forecast sales, start a targeted marketing campaign, and make available resources.This whole scenario can be done without human interference using clustering methodology in machine learning. Products and customers are segmented in clusters and feed to the machine to drive fruitful results. It may result in more accurate outcomes and spare time for employees to focus on the core business process.
Identifying Criminal Activities
In this scenario, the algorithm can detect the probability of fraud or crime occurrence based on individuals’ different behaviors, such as certain social media posts, buying habits, hate speech, and connections with other dubious people or groups over the internet. The challenge comes amidst the recognition of fiction and reality.By analyzing the activities mentioned above of an individual, the algorithm can group similar people according to their behaviors or patterns. Based on the data obtained by evaluating and distinguishing normal and abnormal behavior of individuals in a group. Machines are then able to classify them as criminal or not.
Which players are going to perform best for the national cricket or football team against the specific team? Which player can best suited against the clear rival? Which team or player is likely to exploit the particular weakness of your team? What is the batting and bowling style of a rival team’s players? What are the weaknesses or strengths of the competitor? Answering such questions can help the team selectors station a team that can outperform in the competition with high probability.If you have a very small dataset of players and team performance to train the machine learning model, the role of unsupervised learning comes in. In this scenario, you can label similar players using some of their common attributes using K-Means clustering. It allows us to better station a team and strategy more quickly and gets a competitive edge over the rivals.
Identification of Cancerous cells
Information about patients is not easily available due to confidentiality and privacy issues. Limited knowledge and datasets of Cancerous patients can be best processed using clustering algorithms. A complex data containing a record of both cancerous and non-cancerous patients are evaluated using the clustering algorithms capable enough to distinguish the intricate patterns and features in the data upon which they construct the clusters. Through experimentation, it has been proved such datasets give promising results upon feeding an unsupervised clustering model.
While surfing on search engines like Google, your query responds with similar results that match the criteria. This is one of the best things a clustering can do, grouping similar items, images, or posts in a single cluster and provides that to you. It saves both time and energy on behalf of the server. It doesn’t bother to search the whole database; instead of searching in the clusters that best suit your query, that specific item is likely to be available there. This perfection is attained due to data being assigned to the single cluster providing a comprehensive output to the respective input.
Defense and Security Purpose
The state-of-the-art aspect of technology is always used by military and intelligence agencies. To evaluate the behavior of enemies, predicting their next moves, and formulating defense strategies accordingly is the primary concern of any nation-state. In modern times, the data gathered by intelligence agencies are clustered and passes through intelligent machine learning algorithms to get first-hand knowledge about enemies’ activities by labeling similar activities as the same clusters. This data is shared with the armed forces and state institutions to formulate the policies and strategies further.
There is too much to improve in this domain. The selection of algorithms depends upon the nature of the data collection because each algorithm has its advantage and disadvantages. It means there is no single algorithm that exists to tackle all the clustering challenges.But luckily, there are some algorithms to provide an optimist solution, and more specifically, K-means best serves the process in managing huge datasets. Despite too much space for improvement and advancement in the obscure field of data clustering, the existing clustering methodologies are capable enough to deal with so many problems, as discussed in this article.Let us know if you have used any other data clustering algorithms.