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Data Mining

Data mining is basically the usage of information and data to organize the common data together in groups or categories. It is mainly used by companies that want to satisfy their customers needs by their previous purchases. This helps the companies by getting the best service provided to their customers and the customers get things that are more to their own interests. For example, Wal-Mart is leading the data mining world, they gather information from their many outlets around the countries and allow this information to be accessed by suppliers, in order to best satisfy their massive customer load.

Data mining is split into four types, Classes, Clusters, Associations, and Sequential patterns. Classes are stored data that is located in already determined groups. Clusters, on the other hand, are data that is grouped based on consumer preferences. Associations are data that is used from different sources to associate between them. And Sequential patterns are data that is mined in order to anticipate the behavioral patterns.

In addition to this, Data mining is supported by three main technologies, Massive data collection, Powerful multiprocessor computers, and Data mining algorithms. These three allow Data mining to advance at a steady pace and makes it ready for application in businesses and communities. These three technologies facilitate Data mining at unprecedented rates; they help commercial databases grow with more data.

The scope of Data mining is one to look at from two angels. First being the automated prediction of trends and behaviors. Second is automated discovery of previously unknown patterns. Prediction has become automated with the use of data mining, whenever a pattern is made, data mining predicts it. Discovery is another aspect that has been simplified by data mining, data mining review previous hidden patterns and classifies them.

Furthermore, there are many techniques used in data mining but the most commonly used techniques are Artificial neural networks, which are basically predictive models of a non-linear nature. Another is Rule induction which states that if statistical significance is found in data, a useful if-then rule can be extracted. A third technique is the Nearest neighbor method which basically classifies data to certain groups based on their similarities.

In conclusion, Data mining may seem to be a new thing but it has been used as far as 6 years ago, this helped companies, enterprises and schools. This is by using many techniques and classification for data, which allow data to be organized according to the criteria stated by the user. This makes every bit of data easy to access and edit.

Questions:-

1-how much can we expect data mining to improve before it is spread to the public?

2-what are the biggest fears for data mining in the near future?

3-in what percentage do you think that the evolution of data mining will increase

References:-

1-http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm

2-http://www.thearling.com/text/dmwhite/dmwhite.htm