Data, as we all know, is a collection of raw information. Data is collected for analysis and reference. It can be measured, compared, and manipulated in various ways to extract useful information. Unless the data is converted into useful knowledge or information, it has no meaning. There are other processes that are also performed on data to make it usable for various applications. One of these processes is data mining.

Meaning of Data Mining

When a large set of data is manipulated and analyzed to extract useful information or knowledge, it is called data mining. It is the process of digging through a huge set of data to extract relevant knowledge from a raw data set. It is difficult to extract information manually from the huge data bases that are created these days. Hence, certain tools are used for data mining.

Uses of Data Mining

The main aim for data mining is to predict. Data mining can be used for:

  • Marketing: information extracted through data mining can be used for analyzing the market and its potential.
  • Risk Management: it helps in the detection of fraud and other risks faced by an organization.
  • Customer Relations: helps in analyzing customer behavior and preference so as to provide a better customer experience, back up and support to encourage customer retention.
  • Production: the information extracted via data mining can help in controlling production in industries.
  • Sales: it helps to analyze and highlight the biggest learning from sales.
  • Science: Data mining is useful for scientific inquiry and exploration.
  • Prediction of Trends: Helps to determine the trends and behavior of consumers and provides predictive information that can help businesses.
  • Discover Patterns: It helps to find patterns within the data that are not so easy to recognize. This helps in determining consumer behavior, profiling, identifying consumer requirements and purchasing patterns.

How is data mining done?

There are three stages in data mining, which are:

  • Studying or exploring data – this involves preparing data for analysis and includes processes such as cleaning the data, selecting it and transforming it to make it more manageable.
  • Validation and model selection – this stage consists of choosing the best mode or method on the basis of each mode’s predictive performance. This may mean that different modes may have to be applied to the same data and to compare and to validate their results to come up with the most suitable one.
  • Deployment – this is the final stage where the chosen mode is applied to the data to get desired results that can accurately predict or give estimates.

Tools for Data Mining

The data sets used for data mining can be huge and extensive. It is very difficult to manipulate the data manually and hope to get any credible result. In order to facilitate the process of data mining, there are many tools that are available that extract data using machine learning, artificial intelligence, etc. Some of these tools are available for free on the net and can be used very effectively for data mining. These include:

  • Hadoop
  • RapidMiner
  • Weka
  • Orange
  • Rattle GUI

Data mining has proven very useful in turning up with surprising statistics about certain problems that have provided unique solutions to existing problems. It has created a system through which problems can be studied in a new light and solutions offered to eradicate the source of the problem.