Clustering Algorithms and their Significance in Machine Learning

Clustering is a powerful machine learning method involving data point grouping. With a set of various data points, data scientists can utilize a clustering algorithm to categorize or classify every data point into a particular group. Theoretically, data points present in the same group contain similar features or properties. On the other hand, data points in separate groups contain highly unique features or properties.

Clustering is an unsupervised learning method and is a popular technique among data scientists to obtain statistical data analysis in various fields. People utilize clustering analysis in data science to gain critical insights. They analyze the groups each data point falls into when applying clustering algorithms. Are you new to clustering algorithms and want to learn their ins and outs? Continue reading this article as it discusses everything you must know about clustering algorithm fundamentals.

Significance of Clustering

Clustering algorithms are essential for data scientists to discover innate groupings among unlabeled and labeled data presets. Surprisingly, there are no particular criteria to highlight good clustering. It comes down to individual preferences, requirements, and what a data scientist utilizes to fulfill their need.

Let’s say for example, one could be interested in discovering homogenous group representatives (data reduction), in natural clusters and defining their unknown properties. Some people also wish to find unordinary data objects and other suitable groupings. Whatever the case, this algorithm makes several assumptions constituting similarities between various points. What’s more, every guess makes new but equally well-founded clusters.

Clustering Methods

Hierarchical Based Methods

The clusters created in this procedure create a tree-like structure representing the hierarchy. The new clusters appearing on the tree come from previously formed clumps. Experts divided them into the following categories:

Agglomerative

Bottom-up approach – Each data point is a single cluster and they continuously merge (agglomerate) until all have progressively merged into a single cluster. This process is also known as HAC

Divisive

Top-down approach –  Starting with all data contained in a single cluster, which progressively split until all data points are separate.

Density-Based Methods

Density-based methods conceive clusters as denser regions with some similarities and differences compared to lower dense regions. Methods like these offer excellent accuracy and can combine two clusters with ease.

Grid-Based Methods

Grid-based methods formulate the data space into a limited number of cells forming a structure resembling a regular grid. Every clustering operation happening on these grids are independent and quick.

Partitioning Methods

Partitioning techniques divide the objects, transforming them into k clusters. Each partition creates one cluster. Data scientists often utilize this method to optimize impartial similarity functions, particularly when is a distance is a significant parameter.

What is K-Means Clustering?

K-Means is arguably the most recognized clustering algorithm. Most machine learning and data science courses, especially the introductory classes, teach this algorithm. Understanding it is quite easy, and implementing it in code is even more straightforward. K-Means stands out from other algorithms because of its rapid pace. Most of us are computing distances between group centers and points with minimal computations. So, the complexity is often linear O{n).

Real-World Examples of Clustering Algorithm Uses

The clustering algorithm has been revolutionary in the data science world. Numerous fields are utilizing it and obtaining excellent results. The following are some real-world examples that showcase this algorithm’s usefulness.

Recognizing Fake News

Fake news is nothing new but it is more prevalent compared to a decade ago. Technological innovations are mostly responsible for creating and distributing inauthentic stories on various online platforms. Two students from the University of California used clustering algorithms to recognize fake news.

The algorithm obtained content from various news articles and examined their words. Clusters help the algorithm identify the genuine and disingenuous pieces. The computer science students learned that click-bait articles used sensationalized vocabulary. It indicated that most articles that used sensationalism were not authentic.

Sales and Marketing

Big businesses are all about targeting and personalizing their products. They do this by analyzing the particular characteristics of people and sharing programs to attract them. It is a tried and tested method that helps organizations target specific audiences. Unfortunately, some businesses are unsuccessful in their sales and marketing efforts.

You must target people correctly to get the most out of your investment. You risk significant losses and customer distrust by not analyzing what your audience wants. Clustering algorithms can group individuals with similar traits and analyze whether they will purchase your product. Creating groups can help businesses run tests to determine what they need to do to improve their sales.

Fantasy Sports

You’d be surprised to see how useful clustering algorithms are for fantasy football and various other digital sports. People often have a hard time determining who they should add to their team. Choosing high-performing players, especially during the earlier part of the season, is quite complicated. Why? Because you do not know the athlete’s current form. With little to no performance data at your disposal, you can take advantage of unsupervised learning.

It could help you discover similar players utilizing some of their attributes. K means clustering is particularly handy for such situations, giving you the upper-hand at the league’s start.

Identifying Criminal Activity

While clustering algorithms can help with various criminal activities, let us focus on a taxi driver’s fraudulent behavior. Let us say you want to find whether the driver is lying about his distance traveled per day. How do you identify whether he or she is lying or telling the truth?

Clustering can help you analyze GPS logs and create a group of identical behaviors.You can study the group’s characteristics and classify fraudulent and genuine behaviors.

Spam Filters

Our email inboxes contain junk folders with numerous messages identified as spam. Many machine learning courses utilize the spam filter to showcase clustering and unsupervised learning. Spam emails are arguably the most annoying part of marketing techniques. Some people also utilize them for phishing others’ personal data.

Companies prevent these emails by using algorithms to identify spams and flag them. K means clustering methods have been quite effective in identifying spams. They look at various parts of the email, such as content, sender, and header, to determine if they are junk. It improves accuracy by tenfold and protects people from phishing and other digital crimes.

Final Thoughts

In Summation, clustering mostly remains constant and applies to numerous scenarios. You can make accurate behavioral predictions by using this versatile algorithm. Once you develop a solid baseline of grouped data, the opportunities will be endless.