Data Mining and Knowledge Discovery in Data Mining (KDD)
Data volumes are growing dramatically by the day and every moment. While considering the fields of medical science and astronomy etc we realize that the job of looking into the data is no more like the yester years when it was possible for just human beings to interpret it. Today the sheer volume of data has made it mandatory to at least partially automate the data analysis process. New tools and methods have to be developed to help human beings from getting lost in the ever multiplying universe of digital data. To make any sense of such huge quantities of data, it needs to be made more compact and understandable.
What do the business people do with the data? In the business environment data is primarily used to,
KDD is a term that has evolved from other related fields like machine learning, pattern recognition, statistics, AI, knowledge acquisition, data visualization, and high-performance computing. It is a very interactive process where a lot of decision has to be taken by the user.
The application areas of KDD are,
What do the business people do with the data? In the business environment data is primarily used to,
- Gain competitive advantage
- Give better service to customers
- Increase efficiency
KDD is a term that has evolved from other related fields like machine learning, pattern recognition, statistics, AI, knowledge acquisition, data visualization, and high-performance computing. It is a very interactive process where a lot of decision has to be taken by the user.
The application areas of KDD are,
- Marketing
- Artificial Intelligence
- Finance / Investment
- Fraud detection
- Manufacturing
- Telecommunications
- Internet agents
Labels: data cleaning, data mining, data preparation, data validation, KDD, knowledge discovery



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