Application Of Data Mining.

Data mining is a process that analyzes a large amount of data to find new and hidden information that improves business efficiency. Various industries have been adopting data mining to their mission-critical business processes to gain competitive advantages and help business grows. This tutorial illustrates some data mining applications in sale/marketing, banking/finance, healthcare and insurance, transportation and medicine.

Data Mining Applications in Sales/Marketing

Data mining enables businesses to understand the hidden patterns inside historical purchasing transaction data, thus helping in planning and launching new marketing campaigns in prompt and cost effective way. The following illustrates several data mining applications in sale and marketing.

  • Data mining is used for market basket analysis to provide information on what product combinations were purchased together when they were bought and in what sequence.  This information helps businesses promote their most profitable products and maximize the profit. In addition, it encourages customers to purchase related products that they may have been missed or overlooked.
  • Retail companies use data mining to identify customer’s behavior buying patterns.

Data Mining Applications in Banking / Finance

  • Several data mining techniques e.g., distributed data mining have been researched, modeled and developed to help credit card fraud detection.
  • Data mining is used to identify customers loyalty by analyzing the data of customer’s purchasing activities such as the data of frequency of purchase in a period of time, a total monetary value of all purchases and when was the last purchase. After analyzing those dimensions, the relative measure is generated for each customer. The higher of the score, the more relative loyal the customer is.
  • To help the bank to retain credit card customers, data mining is applied.  By analyzing the past data, data mining can help banks predict customers that likely to change their credit card affiliation so they can plan and launch different special offers to retain those customers.
  • Credit card spending by customer groups can be identified by using data mining.
  • The hidden correlation’s between different financial indicators can be discovered by using data mining.
  • From historical market data, data mining enables to identify stock trading rules.

Data Mining Applications in Health Care and Insurance

The growth of the insurance industry entirely depends on the ability to convert data into the knowledge, information or intelligence about customers, competitors, and its markets. Data mining is applied in insurance industry lately but brought tremendous competitive advantages to the companies who have implemented it successfully. The data mining applications in insurance industry are listed below:

  • Data mining is applied in claims analysis such as identifying which medical procedures are claimed together.
  • Data mining enables to forecasts which customers will potentially purchase new policies.
  • Data mining allows insurance companies to detect risky customers’ behavior patterns.
  • Data mining helps detect fraudulent behavior.

Data Mining Applications in Transportation

  • Data mining helps determine the distribution schedules among warehouses and outlets and analyze loading patterns.

Data Mining Applications in Medicine

  • Data mining enables to characterize patient activities to see incoming office visits.
  • Data mining helps identify the patterns of successful medical therapies for different illnesses.

Data mining applications are continuously developing in various industries to provide more hidden knowledge that  increases business efficiency and grows businesses.

Other Scientific Applications

The applications discussed above tend to handle relatively small and homogeneous data sets for which the statistical techniques are appropriate. Huge amount of data have been collected from scientific domains such as geosciences, astronomy, etc. A large amount of data sets is being generated because of the fast numerical simulations in various fields such as climate and ecosystem modeling, chemical engineering, fluid dynamics, etc. Following are the applications of data mining in the field of Scientific Applications −

  • Data Warehouses and data preprocessing.
  • Graph-based mining.
  • Visualization and domain specific knowledge.

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