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Building the Futuristic AI Models with Clustering Algorithms

The objective of any Artificial Intelligence (AI) engineer team is straightforward: the AI model should be self-sustaining and capable of delivering accurate predictions with the minimum necessary to feed information. Data scientists are working round the clock to build the perfect AI model that can ingest and analyze data with little or no training of dataset. This has given rise to the concept widely called unsupervised learning, and there are various techniques for how you can train an AI model using unsupervised learning algorithms.

One such technique is the “Clustering Algorithm.” In this article on Clustering algorithms (CA’s), we defined what clustering algorithm is; and the various types of CA that are widely used in data science projects.

As a continuation to our cornerstone article, we are moving a step forward and explaining how the future of AI ML science depends on CA techniques and what are the top applications that are currently using this.

Application 1: Differentiating Unsupervised Learning from Supervised Learning

Clustering is also popularly referred to as cluster analysis, and it is considered the first step to succeeding with machine learning with Python programming language.

The number one application of the CA technique is to build unsupervised learning. A majority of ML projects that we work with are mostly supervised data models that can be defined from their “labelled” responses. At the initial stages of data mining, scientists can label an AI ML model, supervised by virtue of two approaches commonly known to every analyst. These are Regression and Classification.

Supervised learning based on Classification techniques is an object differentiating approach that differentiates and classified families into separate categories. For example, a man from a woman. Dogs from cats. Apples from Mangoes, etc, and so on.

All random forests and decision trees are based on these supervised learning approaches to classification. Likewise, the regression approach defines what relationship each family of an object has with dependent and independent variables.

For example, Blood group of males and females in a classroom; breeds of cats and dogs participating in a grooming competition, or, types of fruits used in a dessert or salad to be served to different age groups, etc.

It’s easy to train an ML model using supervised but hard to scale from augmented intelligence – the advanced version of AI that we are all working to achieve in our lifetime.

That’s where unsupervised learning comes into the picture

As the name suggests, unsupervised learning means an ML model that is built without any kind of human intervention. And, it’s possible only with very few techniques. One of them being, Clustering Algorithms!

If you are working on any advanced futuristic AI ML project that requires you to differentiate between results and analysis based on categorical classification of data sets, it’s best to use Clustering. It makes differentiation so easy for any data science team.

Application 2: Building a family of Connected Devices based on Sensors technologies

Let’s talk Edge, IoT, and Connected devices. These alone take up 90% of the futuristic AI ML applications bases on CA techniques.

Since AI ML projects need a huge data storage and processing centers to succeed with computing-intensive operations, scientists are developing Edge on the clusters.

Have you heard of Embedded AI and AI accelerators?

One of the biggest projects in Edge that I have evaluated for Cluster Analysis belongs to IBM.

IBM delivers Clusters at the Edge, and this is the pinnacle of connected infrastructure for all kinds of next-gen devices, including your AI-based voice assistants (Alexa, Siri, etc.), in-car infotainment, IoT semiconductors, AR VR/ e-gaming, and so on. The applications have found their way into MedTech, agro-tech, car-tech, and smart city infrastructures.

Read more:- Google Account Safe

Application 3: Cybersecurity

This is my favourite domain, considering how easily CA techniques can be deployed for anomaly detection. In cyber frauds related to credit cards and identity thefts, we find k-means and DBSCAN getting wide-scale acceptance for cyber security AI ML adoption.

All big IT teams are accelerating their service delivery for cloud modernization and migration using anomaly detection.

If you are working on Edge projects and want to include AI-based IT operations in your set of deliverables, learning clustering algorithms can be very helpful.

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