Data Warehouse Solution

A simple example of data mining technique is the nearest neighbor prediction algorithm is that if you look at the people in your neighborhood (in this case those people that are in fact geographically near to you). You may notice that, in general, you all have somewhat similar incomes. Thus if your neighbor has an income greater than $100,000 chances are good that you too have a high income. Certainly the chances that you have a high income are greater when all of your neighbors have incomes over $100,000 than if all of your neighbors have incomes of $20,000. Within your neighborhood there may still be a wide variety of incomes possible among even your “closest” neighbors but if you had to predict someone’s income based on only knowing their neighbors you’re best chance of being right would be to predict the incomes of the neighbors who live closest to the unknown person.

The nearest neighbor prediction algorithm works in very much the same way except that “nearness” in a database may consist of a variety of factors not just where the person lives. It may, for instance, be far more important to know which school someone attended and what degree they attained when predicting income. The better definition of “near” might in fact be other people that you graduated from college with rather than the people that you live next to.

Nearest Neighbor techniques are among the easiest to use and understand because they work in a way similar to the way that people think – by detecting closely matching examples. They also perform quite well in terms of automation, as many of the algorithms are robust with respect to dirty data and missing data. Lastly they are particularly adept at performing complex ROI calculations because the predictions are made at a local level where business simulations could be performed in order to optimize ROI. As they enjoy similar levels of accuracy compared to other data mining techniques the measures of accuracy such as lift are as good as from any other.

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• Benefit of Using Data Mining

Data Mining had a lot of benefit to any kind of business. The following list just a few sample of benefits of data mining that happens in real-world situations:

1. In a large company manufacturing consumer goods, the shipping department regularly short-ships orders and hides the variations between the purchase orders and the freight bills. Data mining detects the criminal behavior by uncovering patterns of orders and premature inventory reductions.
2. A mail order company improves direct mail promotions to prospects through more targeted campaigns.
3. A supermarket chain improves earnings by rearranging the shelves based on discovery of affinities of products that sell together.
4. An airlines company increases sales to business travelers by discovering traveling patterns of frequent flyers.
5. A department store hikes the sales in specialty departments by anticipating sudden surges in demand.
6. A national health insurance provider saves large amounts of money by detecting fraudulent claims.
7. A major banking corporation with investment and financial services increases the leverage of direct marketing campaigns. Predictive modeling algorithms uncover clusters of customers with high lifetime values.
8. A manufacturer of diesel engines increases sales by forecasting sales of engines based on patterns discovered from historical data of truck registrations.
9. A major bank prevents loss by detecting early warning signs for attrition in its checking account business.
10. A catalog sales company doubles its holiday sales from the previous year by predicting which customers would use the holiday catalog.

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• Example of Nearest Neighbor Technique in Data Mining