Data Mining and Neural Networks

Data Mining and Neural Networks

Data mining is the process used by analysts to find anomalies, correlations, and patterns within large data sets.  A broad range of data mining tools are used by companies to locate essential information.

Data mining is key because:

  • Allows you to sift through the repetitive and chaotic noise in your data
  • It enables us to understand what is relevant and then make excellent use of the information to assess likely outcomes.
  • Data mining accelerates the process of making an informed decision.

Sophisticated data analysis tools such as statistical models, machine learning, and mathematical algorithms are used to discover previously unknown patterns and relationships in large data sets. Apart from collecting and managing data, data mining also involves analysis and prediction

A surge of interest in data mining is being witnessed today because of advances in technology and business processes. Some of these advancements include developments and growth of computer networks used to connect to databases, enhanced search-related techniques, and advanced algorithms. Analysts and researchers are now able to access centralized data resources from a desktop and combine data from disparate sources into a single search source.

What is a neural network?

Our data mining online tutors describe a neural network as the brain metaphor for information processing. Neural networks have shown to be very promising systems in several forecasting and business classification applications. This is because they can learn from the data, are non-parametric in nature, and can generalize.

To understand how a neural network operates, you must first be well-versed in  neural computing, which refers to a methodology of pattern recognition for machine learning. Neural computing usually gives forth to a model called an artificial neural network or a neural network. This model is used in a variety of business applications for forecasting, pattern recognition, classification, and prediction. Every data mining toolkit has a neural network as an essential component.

A neural network is biologically inspired. It imitates the neuron structure of a man and uses the concept of how two biological cells are interconnected. A neural network is a distributed matrix structure based on the M-P model and the Hebb learning rule. In Data mining, it is used for clustering, classification, feature mining, pattern recognition, and prediction. We can broadly divide the neural network into three:

  • Feed-forward networks

This type of neural network uses the function network and perception back-propagation as representatives. They are mainly used in the areas of pattern recognition and prediction.

  • Feedback network

The feedback network uses the Hopfield discrete model and continuous model as representatives. It is mostly used for optimization calculation and associative memory.

  • Self-organization network

The self-organization network regards the Kohonen mode and the adaptive resonance model (ART) as representatives. It is usually used for cluster analysis.

In simple and practical terms, we can see neural networks as non-linear statistical data modeling tools. Neural networks can be used to model intricate relationships between inputs and outputs or find patterns in data. Data warehousing firms are using neural networks to harvest information from datasets. This process is what we call data mining.

Neural networks comprise of three components:

  • The architecture which is also called the model
  • The learning algorithm
  • The activation functions

The network is programmed to store, recognize, and retrieve patterns. This allows us to solve combinatorial optimization problems, filter noise from data, and control ill-defined problems. In summary, a neural network is used in data mining to estimate sampled functions when we do not know the form of the functions. Pattern recognition and function estimation are the two functions that make a neural network a prevalent utility in data mining. With data sets growing in sizes every day, there is a need for automated processing. Neural networks boast of model-free estimators and dual-nature that serve data mining in several ways.

The data mining process based on a neural network

The process of data mining can be divided into three phases:

  • Data preparation
  • Data mining
  • Expression and interpretation of results

Data preparation is meant to make the data fit for the data mining process. It includes the following four processes:

  • Data cleaning
  • Data option
  • Data preprocessing
  • Data expression

Data mining based on a neural network can only handle numerical data. As a result, it must transform the said data into numeric. It is also complex to adopt an appropriate hash function to generate a unique numerical data.

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