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TRENDS IN DATA MINING

What is Data Mining:

  •  Data mining is the process of extraction of interesting patterns or knowledge from huge amount of data.
  • Using information contained within data warehouse, data mining can often provide answers to questions. Like….
    • Which products should be promoted to a particular customer?
    • Which securities will be most profitable to buy or sell during the next trading session?

Application of data mining:

Here is the list of areas where data mining is widely used −

  • Healthcare
  • Finance
  • Retail industry
  • Telecommunication
  • Text Mining & Web Mining
  • Higher Education

Trends in data mining:

  • Application Exploration
  • Scalable data mining methods
  •  Combination of data mining with database systems, data warehouse systems, and web  database systems
  • Standardization of data mining language
  •  Visual data mining
  • New methods for mining complex types of data
  • Web mining

Application Exploration of Data Mining:

  • Earlier data mining was mainly used for business purpose, to overcome the competitors.
  • Data mining is gaining wide acceptance in other fields also. And many new explorations are being done for this purpose.
  • Data mining for business continues to expand as e-commerce and marketing becomes mainstream elements of the retail industry.

Scalable Data Mining Methods:

  • As data is expanding at a massive rate, there is a need to develop new data mining methods which are scalable and can handle different types and large volume of data.
  • The data mining methods should be more interactive and user friendly.
  • One important direction towards improving the repair efficiency of the timing process while increasing user interaction is constraint-based mining.

Combination of data mining with database, data warehouse, and web database systems:

  • Database, data warehouse, and WWW are loaded with huge amounts of data and are major information processing systems.
  • The desired architecture for data mining system is the tight coupling with database and data warehouse systems.
  • Transaction management query processing, online analytical processing and online analytical mining should be integrated into one unified framework.

Standardization of data mining language: 

Today few data mining languages are commercially available in the market like Microsoft’s SQL server 2005, IBM Intelligent Miner and many more…..

Standard data mining language or other standardization efforts will provide the orderly development of data mining solutions, improved interpret-ability among multiple data mining systems and functions.

Visual Data Mining:

  • The result of the mined data can be shown in the visual form So it will further enhance the worth of the mined data.
  • Visual data mining is an effective way to discover knowledge from huge amounts of data.
  • The systematic study and development of visual data mining techniques will promote the use for data mining analysis.

Web Mining:

  • The WWW is globally distributed collection of news, advertisements, consumer records, financial, education, government, e-commerce and many other services.
  •  The WWW also contains huge and dynamic collection hyper linked information, providing a large source for data mining.
  • Based on the above facts, the web also poses great challenges for efficient resource and knowledge discovery.

Need of Data Mining:

  •  The massive growth of data from terabytes to perabytes is due to the wide availability of data in automated form from various sources as WWW, Business, Society etc….
  • We are drowning in data but deficient of knowledge Data is useless, if it cannot deliver knowledge.
  • That is why data mining is gaining wide acceptance in today’s world.

 

Awadhesh Kumar

Jain Software Developers