Data Mining

Reviewed by Anjaneyulu | Updated on Aug 27, 2020

Introduction

Data mining in huge data sets searches for secret, true, and potentially useful patterns. Data Mining is about finding between the data unsuspected/previously unknown relationships. It is a multidisciplinary skill that uses the technologies of machine learning, statistics, AI, and databases.

The insights obtained from data mining can be used for marketing, identification of fraud, and scientific discovery. Data mining is also called the discovery of knowledge, extraction of knowledge, analysis of data/patterns, processing of information, etc.

Understanding with example

A bank wants to look for new ways to boost revenue from its credit card operations. They want to test that use will double if fees are halved. Bank's record on average credit card balances, payment amounts, use of credit limits, and other vital parameters is several years.

They create a model for testing the effect of the proposed new company policy. Results of the data indicate that cutting fees in half for a focused client base might boost revenues.

Advantages

  • Data mining technique helps companies get information based on knowledge.
  • Data mining lets companies make successful organizational and manufacturing adaptations.
  • Compared to other statistical applications, data mining is a cost-effective and efficient solution.
  • Data mining helps in decision-making.
  • It facilitates automatic trend and activity analysis, as well as automated hidden pattern discovery.
  • It can be introduced in new systems as well as current applications. It is a quick process that makes analyzing vast amounts of data in less time convenient for users.

Disadvantages

  • There are chances that businesses will sell useful information about their customers to other firms for money.
  • Most program for data mining analytics is challenging to run and requires specialized training to work.
  • Different tools for data mining work in different ways, due to various algorithms used in their design. Choosing the right data mining method is, therefore, a challenging task.
  • The techniques of data mining are not reliable and can cause severe consequences under certain conditions.