An Introduction To Data Mining
Almost everyone needs information today. While there is data aplenty in the world, getting proper information out is a herculean task This is where data mining comes in. Today data mining is used to extract relevant information from heaps and mounds of data available from the Internet and other sources. It is used in business and technology sector for ongoing research in a number of areas. Data mining involves analysis of available data using predefined mathematical algorithms. The main concept behind data mining is to look for identifiable patterns which occur frequently. Take the example of data about the customers of a certain product. We have the annual sales data of that product from two areas and we want to predict when can we make most benefits. In order to make more benefits we have to ship more products when they are in high demand.
Data mining allows us to determine when did the product sale go highest in a particular. If the sales were high in first area in the first half of the year and in the second area in the second half, than we can simply send more products to first area in first half and second area in second half. Data mining helped us determine when were the sales at peak and we were able to make more profits. Data mining is also one of the most accurate modes of data collection and analysis. We know that data analysis is done using preset mathematical algorithms. With the development of computers, data mining became one of the first uses. Programs were written for database management and the data mining algorithms were implemented in them. Being defined by the mathematical rules, the data is analyzed in a very accurate manner. Earlier techniques of research were marred by the bias of researchers and analysts. This is not a possibility in data mining because both research and analysis is done by a machine. If the algorithm is implemented correctly, there is no possibility that any bias can mar the accuracy of results of data mining. In the recent times some criticism of data mining has also surfaced. The two major grounds on which data mining has been criticized are privacy concerns and data dredging. A number of privacy questions can be raised if someone tries to do data mining from a database containing personally identifiable information without first seeking the permission of that individual. Similarly data dredging is the attempt at data mining to create imaginary relationships. Take the example of someone probing crucial law enforcement or security related data like Lexis Nexis data and we are talking about some serious trouble! But as is the case with any other technology, the person who uses it negatively is to be blamed. Similarly data mining can be a tool or a weapon, depending upon who wields it. |
