What is data mining?


Data mining is the extraction of interesting (non-trivial, implicit, previously unknown, potentially useful) information or pattern from data of large database.


It is a well-known secret that the competition in the telecommunication industry is fierce. The acquisition of new customers is difficult and often very expensive. Subsequently customer retention has become more and more important. Data Mining can determine characteristic customer clusters on the basis of collected historic data points from customers - such as for instance the frequency and timely distribution of customers' usage of services (calls, text messages, MMS, navigation, mail exchange,...). For each of these customer patterns the company can then offer tailored customer-life-cycle messages and offers.

The example shows how data mining can help a telecommunication service provider to customise their offers. This leads to higher customer satisfaction as well as to an increase in turnover and profit by risen sales over the whole customer life cycle (lifetime value).

Goals of data mining

Following are the goals of data mining:
  • prediction
  • identification
  • classification
  • optimization

Data mining as interdisciplinary subject

figure: data mining as interdisciplinary subject

 Data mining tasks

There are mainly two types of data mining tasks. They are:
  1. Predictive tasks: It performs inferences of the data in the database.
  2. Descriptive tasks: It describes the general features of data in the database.

Applications of data mining

Following are the applications of data mining
  • Retail Business
  • Financial data analysis
  • Telecommunication
  • Fraud detection and unusual pattern detection
  • Corporate analysis and risk managmement
  • Biomedical data engineering
  • Intelligent query system
  • Research  etc...


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