How Artificial Neural Networks Can Be Used for Data Mining

Like any mining effort, data mining is the process of extracting something of value from a vast amount of raw material. In this case, it’s obtaining useful information from a sizable volume of data.¹ Once this happens, we’re able to create other valuable products.

As businesses continue to accrue exponentially larger and larger quantities of data, there is a corresponding and critical need for automated processes to handle and make sense of such volumes of information.² For companies keen to mine effectively and understand big data, neural networks in data mining would be an inspired choice. The importance of neural networks, or nodes, is clear in their ability to detect and assimilate relationships between a range of variables.³

Why use neural networks?

A neural network is a series of algorithms that recognize underlying relationships in a set of data through a process that imitates the way the human brain operates.

The artificial neural network (ANN) assimilates data in the same way the human brain processes information. The brain’s neurons process information in the form of electric signals. External information, or stimuli, is received and processed, and the brain then produces an output.⁴

Similarly, neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of artificial intelligence (AI), machine learning, and deep learning.⁵

This process mimicry is achieved in three steps:

• Step 1: ANNs receive input through several processors that operate simultaneously and are arranged in tiers
• Step 2: The first tier receives the raw input data, which it then processes through interconnected nodes that have their own sets of knowledge and rules
• Step 3: The processor then passes it on to the next tier as output. Each successive tier of processors and nodes receives the output from the tier preceding it and processes it further. This refines the data incrementally rather than having to process the raw data anew every time⁶

Neural networks are heuristic, in that they modify themselves as they learn – both from their initial vigorous training and from the continual self-learning they experience by processing additional information.⁷ A simple learning model applied by neural networks involves weighting input streams according to which will be the most likely to be accurate.⁸ Preference is then given to input streams with a higher weight, because these have a greater influence and will almost certainly reduce predictable errors through weight. This is done through gradient descent algorithms.⁹

The process ends with the output units. This is where the network responds to the initial data that was entered, which can now be processed.¹⁰

Neural network advantages in business

Most companies recognize their data as an important asset in their business’s wider operational decision-making. As technology grows, businesses are leveraging neural networks for predictive analytics to fully harness the benefits of data streams.¹¹

ANNs can learn and model non-linear and complex relationships, and they can manage the relationship between inputs and outputs, as this is rarely simple. ANNs also don’t restrict on the input variables, unlike other prediction techniques (such as how they should be distributed).¹²

Here are three ways neural networks are applied successfully across many industries and organizations:¹³

Forecasting

Most departments in a business rely heavily on forecasting data daily to manage their operations, including sales, stock, and workforce. Forecasting problems are typically complex and traditional forecasting models limit data to control these non-linear relationships. When applied correctly, ANNs can effectively forecast without limitations on data through extracting unseen features and defining relationships by means of modeling.

Character and image recognition

ANNs can process a multitude of inputs in hidden and complex, non-linear relationships. This positions them perfectly for character recognition, such as handwriting, which is being effectively used in fraud detection and even national security assessments.

The following are all possible through ANNs:

• Image recognition (such as facial recognition on social media platforms)
• Cancer detection for healthcare industries
• Satellite imagery processing for agricultural and military use

Data mining

Neural networks are often used for effective data mining, turning raw data into viable information. They look for patterns in large batches of data, allowing businesses to learn more about their customers, which can inform their marketing strategies, increase sales, and lower costs.¹⁴

What is data mining?

When organizations employ data mining, their aim is to analyze data against a set of criteria to categorize it into information they can use in defined actions,¹⁵ such as in their business and operational strategies.¹⁶

Before we can understand how neural networks can be used in data mining, you need to be aware of the general requirements for data mining:

1. Understand your business

In order to create accurate data mining goals, it’s important to first understand your business’s goals and needs, and its current situation. Only then can you draft a feasible data mining plan.¹⁷

    2. Collect the data

To leverage data, you need to collect, load, and integrate the data from all available sources.¹⁸

   3. Prepare the data

This part of the data mining process requires time and attention. Before deeper data exploration can take place, the data needs to be mined by software that can clean, construct, and format it. Start by working on a sample segment of the data and then iteratively apply various preparation steps, filtering and cleansing to get the data. This ensures its usability.

   4. Select a model

Once the prepared data set is complete, you need to select the modeling techniques you’ll use. You’ll need to create test scenarios to test the validity of the models you have selected, and all operational stakeholders should be involved in assessing these. The best test scenario will be the one you choose for the business and should be applied to the entire data set.¹⁹

   5. Evaluate the data

Data mining is defined by the patterns that emerge from it. This may mean the initial business objectives you originally identified will need to re-evaluated and revised. Data mining is an iterative process and the business understanding you gain through it is continuous.

   6. Present the data

Ultimately, you’ll need to present or deploy the business insights revealed by the data mining process. It’s important that you do this in such a way that stakeholders can use the information effectively.²⁰

Neural networks in data mining

Neural networks mine data in areas such as bioinformatics, banking, and retail.

Using neural networks, data warehousing organizations can extrude information from datasets to help users make more informed decisions. This is achieved through a neural network’s ability to handle complex relationships, cross-pollination of data, and machine learning. Neural networks and AI technologies can carry out many business purposes with unstructured data, from tracking and documenting real-time communications, to finding new customers that automate follow-ups and flag leads.²¹

Until recently, decision-makers had to rely primarily on extracted data from structured, highly organized data sets, as these are easier to analyze. Unstructured data like emails and copy are more difficult to analyze and so have gone unutilized or simply ignored. Neural networks can now provide decision-makers with much deeper insight into the ‘why’ of a customer’s behavior, which goes beyond what is provided in more structured data.²²

It’s essential that you exploit the value that automation can bring to your business. The Artificial Intelligence: Implications for Business Strategy online short course from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) will empower you to seamlessly introduce and integrate AI and machine learning (ML) in your business strategy.

In healthcare, there are several types of neural networks making an impact:²³

• The first type of neural network impacting the healthcare industry is a Convolutional Neural Network (CNN). In the world of neural networks, CNNs are widely used for image classification
• Then there is the Recurrent Neural Network (RNN), where the sequence of the data matters, such as in verbal communication. Natural Language Processing (NLP) is a common technique used in RNNs to build voice recognition applications. Short-term automation through AI will help with dictation and transcription via the use of virtual assistants. Doctor’s notes will be captured and transcribed in near real-time
• Lastly, there is the Generative Neural Network (GAN), which is actually two neural networks: one is a generator that creates fake data and the second is a discriminator, which attempts to tell if the data is real or fake. The process pitting the generator and discriminator against each other helps build better outcomes for the models. GANs are being used now to speed along the discovery phase of the approval process. Researchers can generate a list of known elements for use in a GAN to build out millions of different possibilities for element combinations, which will be the next to treat breast cancer, prostate cancer, or other diseases

But these benefits aren’t restricted to one industry or application. For instance, the most frequent example of artificial neural network application in e-commerce is in personalizing the purchaser’s experience. Amazon, AliExpress, and other e-commerce platforms use AI to show related and recommended products. The recommendation is formed on the basis of the users’ behaviors. The system analyzes the characteristics of certain items and shows similar ones. In other cases, it defines and remembers the person’s preferences and shows the items meeting them.

In finance, there are neural network applications for fraud detection, management, and forecasting. For example, SAS Real Time Decision Manager helps banks find solutions when analyzing risks and probable profits, such as whether to approve credit.²⁴

ANNs have become a trusted and useful tool for finding solutions within unstructured data, thanks to their ability to make sense of nonlinear processes. Various problems are now being solved, making it easier for decision-makers to know the correct way forward, and take more confident and sustained strides towards their business’s future.