Artificial Neural Networks in Data Mining: Applications, Examples & Advantages

In the information age, data has become a gold-dust. A commodity which cannot be taken lightly, and is highly valuable. It takes time and effort to mine this resource, and there is a need to automate this process.

Neural Networks find extensive applications in areas where traditional computers don’t fare too well.

Neural Networks can detect, understand, and incorporate relationships between a large number of variables. Applications of neural network in the real world are wide. This has become essential for businesses that effectively mine and interpret big data.

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What is Data Mining?

Data Mining is a process by which companies turn raw data into useful information. Softwares are used to look for patterns in large groups of data, and it helps businesses in terms of learning about customers. You can make informed marketing strategies, cut down on costs, and increase sales.

Here are some examples of how organizations use data mining to serve you better:

• Amazon – The list of recommended products you get after buying something on Amazon is one of the prime examples of Machine Learning and AI. Without feeding relevant data and patterns, it would’ve been impossible to extract these data points.
• Service Providers – Mining and analysis of data that service providers have on their customers gives companies a probability score. This is how companies focus on customers who are at a higher risk of switching vendors and provide them personalized attention.
• Crime Prevention – The details regarding major criminal activities that have happened in the past and thoroughly studying the patterns. This process helps the Law Enforcement sector to predict future events and identify where the next threat could be coming from.

What are Neural Networks?

The way human brain processes information is how Artificial Neural Networks (ANN) bases its assimilation of data. The brain has neurons process information in the form of electric signals.

In the same way, ANN receives input of information through several processors that operate in parallel and are arranged in tiers.

The raw data is received by the first tier, which is processed through interconnected nodes, having their own rules and packages of knowledge.

The processor passes it on to the next tier as output. All such successive tier of processors receive the output from its predecessor; therefore, raw data isn’t processed every time.

The Neural Networks modify themselves as they are self-learning after processing additional information. Each link between nodes is associated with weights.

A preference is put on the input stream with higher weight. The higher the weight of the unit, the more influence it has on another. It helps in reducing predictable errors, and it is done through a gradient descent algorithm.

Use of  Artificial Neural Networks in Business

Companies now understand that data that they possess can help them provide information when it comes to decision making. Businesses are leveraging neural networks to utilize the benefits of data streams.

ANNs have the ability to learn and model non-linear relationships. Unlike other prediction techniques, it doesn’t impose restrictions on input variables.

Here’s how industries and organizations apply neural networks to gain an advantage:

1. Forecasting of Data

Traditionally forecasting models have limitations to data, and such problems are complex. If ANN is applied correctly, ANNs forecasts without such limitations, as its modeling ability is able to define relationships and extract unseen features.

2. Character – Image Recognition

Since ANN can take a multitude of inputs and can process them in complex non-linear relationships, this makes them ideally positioned for character recognition, such as handwriting. This can, in turn, be used as a fraud detector. The same goes for image recognition – for facial recognition on social media, cancer detection in the field of healthcare, and satellite imagery for agriculture.  

Artificial Neural Networks for Data Mining

Neural networks help in mining data in various sectors such as banking, retail, and bioinformatics. Finding information that is hidden in the data is challenging but at the same time, necessary. Data warehousing organizations can use neural networks to harvest information from data sets.

This helps users to make more informed decisions through neural networks. ANNs can carry out business tasks with structured data. They can range from tracking and documenting real-time communications to finding new leads or potential customers.

As a matter of fact, until recently, decision-makers relied on extracted data from organized data sets. Even though these are easier to analyze, they don’t offer a more in-depth insight as the unstructured data does.

Neural networks provide information such as looking into the ‘why’ of a particular customer’s behavior. Neural Network Step by Step Guide

Let’s take a look at real-life examples of Artificial neural network’s applications in Data Mining:

 1. Healthcare

Neural networks analyzed 100,000 records of patients who were in the Intensive Care Unit (ICU), and it learned to apply experience to diagnose the ideal course of treatment. 99% of these recommendations matched and sometimes improved a doctor’s decision.

 2. Social Media

Business and employment-oriented website in LinkedIn use neural networks to pick up spam or abusive content. LinkedIn also uses it to understand all kinds of content shared, so they can build a better recommendation and search parameter for their members.

Conclusion

Thanks to their ability to model non-linear processes, artificial neural networks are becoming a more trusted and useful tool. Issues such as classification, clustering, regressions, and pattern recognition are unstructured data. They are getting resolved easily, and it is making it easier for decision-makers to gauge the way forward. Furthermore, they can make more calculated strides towards the future of their business.

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