A Critical Overview of Data Mining for Business Applications

Everybody looks to a world that does not remain the same. Furthermore no one can deny that the world is changing, and changing very fast.Technology, education, science, environment, health, communicating habits, entertainment, eating habits, dress - there is hardly anything in life that is not changing. Some changes we like, while others create fear and anxiety around us. Everywhere there is a feeling of insecurity.What will happen to us tomorrow, or what will happen to our children, are questions we keep frequently asking. One thing, however, is clear.It is no more possible to live in the way we have been living so far. It seems that now the entire fabric of life will have to be changed. Life will have to be redesigned. The life of the individual, the social structure, the working conditions and governance all will have to be re-planned.Furthermore over the past 2-3 decades there has been a huge increase in the amount of data being stored in databases as well as the number of database applications in business and the scientific domain. This explosion in the amount of electronically stored data was accelerated by the success of the relational model for storing data and the development and maturing of data retrieval and manipulation technologies. While technology for storing the data developed fast to keep up with the demand, little stress was paid to developing software for analysing the data until recently when companies realized that hidden within these masses of data was a resource that was being ignored. The huge amounts of stored data contains knowledge on a good number of aspects of their business waiting to be harnessed and used for more effective business decision support. Database Management Systems (DMS) used to manage these data sets at present only allow the user to access information explicitly present in the databases i.e. the data. The data stored in the database is only a small part of the 'iceberg of information' available from it. Contained implicitly within this data is knowledge about a number of aspects of their business waiting to be harnessed and used for more effective business decision support. This extraction of knowledge from large data sets is called Data Mining or Knowledge Discovery in Databases and is defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. Almost in parallel with the developments in the database field, machine learning research was maturing with the development of a number of sophisticated techniques based on different models of human learning.Learning by example, cased-based reasoning, learning by observation and neural networks are some of the most popular learning techniques that were being used to create the ultimate thinking machine.