By this point in time, you've probably heard a good deal about data mining -- the database industry's latest buzzword. What's this trend all about? To use a simple analogy, it's finding the proverbial needle in the haystack. In this case, the needle is that single piece of intelligence your business needs and the haystack is the large data warehouse you've built up over a long period of time.
Data Mining in Business
Through the use of automated statistical analysis (or "data mining") techniques, businesses are discovering new trends and patterns of behavior that previously went unnoticed. Once they've uncovered this vital intelligence, it can be used in a predictive manner for a variety of applications. Brian James, assistant coach of the Toronto Raptors, uses data mining techniques to rack and stack his team against the rest of the NBA. The Bank of Montreal's business intelligence and knowledge discovery program is used to gain insight into customer behavior.
Gathering Data
The first step toward building a productive data mining program is, of course, to gather data! Most businesses already perform these data gathering tasks to some extent -- the key here is to locate the data critical to your business, refine it and prepare it for the data mining process. If you're currently tracking customer data in a modern DBMS, chances are you're almost done. Take a look at the article Mining Customer Data from DB2 Magazine for a great feature on preparing your data for the mining process.
Selecting an Algorithm
At this point, take a moment to pat yourself on the back. You have a data warehouse! The next step is to choose one or more data mining algorithms to apply to your problem. If you're just starting out, it's probably a good idea to experiment with several techniques to give yourself a feel for how they work. Your choice of algorithm will depend upon the data you've gathered, the problem you're trying to solve and the computing tools you have available to you. Let's take a brief look at two of the more popular algorithms.
Regression
Regression is the oldest and most well-known statistical technique that the data mining community utilizes. Basically, regression takes a numerical dataset and develops a mathematical formula that fits the data. When you're ready to use the results to predict future behavior, you simply take your new data, plug it into the developed formula and you've got a prediction! The major limitation of this technique is that it only works well with continuous quantitative data (like weight, speed or age). If you're working with categorical data where order is not significant (like color, name or gender) you're better off choosing another technique.
Classification
Working with categorical data or a mixture of continuous numeric and categorical data? Classification analysis might suit your needs well. This technique is capable of processing a wider variety of data than regression and is growing in popularity. You'll also find output that is much easier to interpret. Instead of the complicated mathematical formula given by the regression technique you'll receive a decision tree that requires a series of binary decisions. One popular classification algorithm is the k-means clustering algorithm. Take a look at the Classification Trees chapter from the Electronic Statistics Textbook for in-depth coverage of this technique.
Other Techniques
Regression and classification are two of the more popular classification techniques, but they only form the tip of the iceberg. For a detailed look at other data mining algorithms, look at this feature on Data Mining Techniques or the SPSS Data Mining page.
Data Mining Products
Data mining products are taking the industry by storm. The major database vendors have already taken steps to ensure that their platforms incorporate data mining techniques. Oracle's Data Mining Suite (Darwin) implements classification and regression trees, neural networks, k-nearest neighbors, regression analysis and clustering algorithms. Microsoft's SQL Server also offers data mining functionality through the use of classification trees and clustering algorithms. If you're already working in a statistics environment, you're probably familiar with the data mining algorithm implementations offered by the advanced statistical packages SPSS, SAS, and S-Plus.
Moving On
Have we whetted your appetite for data mining knowledge? For a more detailed look, check out the excellent slide show presentations and other data mining resources on Megaputer.com. If you're ready to get started but can't find any sample data, take a look at the various repositories listed in Data Sources for Knowledge Discovery. Good luck with your data mining endeavors! Stop by our forum and let us know how things are going!
SOURCE:http://databases.about.com/od/datamining/a/datamining.htm
Data Mining in Business
Through the use of automated statistical analysis (or "data mining") techniques, businesses are discovering new trends and patterns of behavior that previously went unnoticed. Once they've uncovered this vital intelligence, it can be used in a predictive manner for a variety of applications. Brian James, assistant coach of the Toronto Raptors, uses data mining techniques to rack and stack his team against the rest of the NBA. The Bank of Montreal's business intelligence and knowledge discovery program is used to gain insight into customer behavior.
Gathering Data
The first step toward building a productive data mining program is, of course, to gather data! Most businesses already perform these data gathering tasks to some extent -- the key here is to locate the data critical to your business, refine it and prepare it for the data mining process. If you're currently tracking customer data in a modern DBMS, chances are you're almost done. Take a look at the article Mining Customer Data from DB2 Magazine for a great feature on preparing your data for the mining process.
Selecting an Algorithm
At this point, take a moment to pat yourself on the back. You have a data warehouse! The next step is to choose one or more data mining algorithms to apply to your problem. If you're just starting out, it's probably a good idea to experiment with several techniques to give yourself a feel for how they work. Your choice of algorithm will depend upon the data you've gathered, the problem you're trying to solve and the computing tools you have available to you. Let's take a brief look at two of the more popular algorithms.
Regression
Regression is the oldest and most well-known statistical technique that the data mining community utilizes. Basically, regression takes a numerical dataset and develops a mathematical formula that fits the data. When you're ready to use the results to predict future behavior, you simply take your new data, plug it into the developed formula and you've got a prediction! The major limitation of this technique is that it only works well with continuous quantitative data (like weight, speed or age). If you're working with categorical data where order is not significant (like color, name or gender) you're better off choosing another technique.
Classification
Working with categorical data or a mixture of continuous numeric and categorical data? Classification analysis might suit your needs well. This technique is capable of processing a wider variety of data than regression and is growing in popularity. You'll also find output that is much easier to interpret. Instead of the complicated mathematical formula given by the regression technique you'll receive a decision tree that requires a series of binary decisions. One popular classification algorithm is the k-means clustering algorithm. Take a look at the Classification Trees chapter from the Electronic Statistics Textbook for in-depth coverage of this technique.
Other Techniques
Regression and classification are two of the more popular classification techniques, but they only form the tip of the iceberg. For a detailed look at other data mining algorithms, look at this feature on Data Mining Techniques or the SPSS Data Mining page.
Data Mining Products
Data mining products are taking the industry by storm. The major database vendors have already taken steps to ensure that their platforms incorporate data mining techniques. Oracle's Data Mining Suite (Darwin) implements classification and regression trees, neural networks, k-nearest neighbors, regression analysis and clustering algorithms. Microsoft's SQL Server also offers data mining functionality through the use of classification trees and clustering algorithms. If you're already working in a statistics environment, you're probably familiar with the data mining algorithm implementations offered by the advanced statistical packages SPSS, SAS, and S-Plus.
Moving On
Have we whetted your appetite for data mining knowledge? For a more detailed look, check out the excellent slide show presentations and other data mining resources on Megaputer.com. If you're ready to get started but can't find any sample data, take a look at the various repositories listed in Data Sources for Knowledge Discovery. Good luck with your data mining endeavors! Stop by our forum and let us know how things are going!
SOURCE:http://databases.about.com/od/datamining/a/datamining.htm
