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Neural Networks Application to Data Mining and Data Warehouse


Like any other mining effort, data mining is extracting and obtaining valuable information from many raw materials, such as a substantial volume of data. Valuable products are then created once this happens. As companies gain more data, an automated process to handle such volumes of information is needed. Neural networks in data mining are an inspired choice for companies that want to understand big data and mine successfully. This is because neural networks can detect and assimilate various variables’ relationships.

Why use Neural Networks?

An artificial neural network (ANN) assimilates data like how a human brain integrates information. Just as a brain produces an output after receiving and processing external stimuli, so does a neural network identify patterns and solve problems common in artificial intelligence. According to Scardapane and Wang(2017), there are three steps that a neural network follows when mimicking the brain;

  • First, it receives input from various processors that are arranged in tiers and operate simultaneously.
  • In the second step, the interconnected nodes process the raw input data received in the first tier.
  • Data is passed to the next tier as output in the third step. Each tier receives output from the preceding tier and then processes it further.

Neural networks modify themselves as they learn. An example of a learning model applied is a neural network weighing input streams according to the one that is likely going to be the most accurate. Streams with a higher stream are given preference since they will reduce predictable errors using gradient descent algorithms. The process ends with the network responding to the initial data entered and can now be processed into output units.

Neural Networks in Data Mining

Neural networks mine data in banking, bioinformatics, and retail (Osman,2019). Using them can assist data warehousing organizations in making informed decisions by extruding information from their data sets. The ability of a neural network to handle cross-pollination of data, complex relationships, and machine learning helps carry out these purposes.

A neural network is applied in data mining in ways such as fraud detection and healthcare. In fraud detection, it has become relatively easy for fraudsters to exploit banks and businesses using technology. However, a neural network helps in detecting fraud before it happens. In healthcare, they help in diagnosing diseases. The neural network can process large datasets of diseases and diagnose them in the early stages. The neural network model in data mining is divided into three types;

  • Feed-forward Neural networks whereby information only moves in one direction from the input node (forward) through hidden nodes to the output nodes.
  • Feedback Neural Networks are used for content addressable memory and allow signals in a feedback network to travel in both directions.
  • Self-Organization Neural Network is trained using competitive learning, unlike other networks that use error correction. It is used to produce a two-dimensional representation of a higher-dimensional data set while still conserving the data’s topological structure.


Until recently, decision-makers primarily relied on extracted data from highly organized and structured data sets as they are easier to analyze. This meant that unstructured data such as copy and emails, which is more challenging to analyze, was left unutilized and ignored. Using neural networks in data mining has provided more profound insight into an organization’s information and customer behavior. This is better and more convenient than what is provided in more structured data. It is, therefore, important for organizations to exploit and consider the value that automation brings to the business.


Osman, A. S. (2019). Data mining techniques.

Scardapane, S., & Wang, D. (2017). Randomness in neural networks: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery7(2), e1200.


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