If you are interested in using data mining, it is important to have an understanding of how it works. The method that is used with data mining to make predictions is called modeling. Modeling is the process of creating a model.
A model can be defined as a number of examples or a mathematical relationship. The relationship or examples are created based on existing situations where an answer is already known. The purpose of data mining is to take the model and place it in a situation where the answer is unknown. While the mathematics behind data mining has existed for centuries, it is the recent increase in computer processing power and storage that has allowed data mining technology to become feasible.
To use an example of creating a model, imagine if you are the head of marketing for a telecommunications company. You have decided that you want to direct your advertising and sales towards people who are the most likely to use long distance communication services. While you may have a knowledge of your own customers, it will be difficult to learn all the different attributes related to your best customers because there are too many variables to take into consideration.
However, if you have a database which has information related to the income, credit history, age, sex, and occupation of your customers, you can use data mining tools to find the common attributes that are related to the customers that frequently make long distance phone calls.
The use of data mining may allow you to learn that most of your high value customers are middle aged women that are 45 years of age. You may also find that these women have an average income that is in excess of $50,000 a year. Now that you know a bit about your best customers, you can now tailor your advertising efforts to suit their needs. By doing this, you will greatly increase your chances of earning a profit. Computer algorithms are frequently used in data mining programs. However, the factors which have led to the increasing popularity of data mining technologies are the increase in both processing power and storage.
Another thing that has led to the rapid popularity of data mining technology are graphical interfaces. These interfaces have made the programs easier to use, and this has allowed them to be adapted by a larger segment of the population. Artificial neural networks are a cutting edge technology that is being used more in data mining applications.
Unlike computer algorithms, neural networks are not linear, and are capable of learning. Neural networks are modeled after the human mind, and have powerful applications in data mining that have not been fully explored. In addition to this, decision trees play an important role in the development of data mining programs.
As the name implies, decision trees are structures have a number of different decisions. Each decision could be called a branch. The decisions define the rules for a given set of data. The next element that makes up an important part of data mining is called rule induction . A rule induction will pull rules from data which are based on an "if-then" scenario.
The next part that makes up data mining is a genetic algorithm. The genetic algorithm will utilize techniques that are based on mutation and natural selection. The last important part of data mining tools is called the nearest neighbor. The nearest neighbor will categorize records with other records that are similar within a database.
There are a number of real-world applications of data mining programs. Generally, having information which is highly detailed will allow you to make predictions that are equally detailed. Using this detailed information to make predictions about the behavior of your customers can allow you to make large profits.
Companies can use data mining tools to get answers to complex questions. For example, a credit card company that wants to increase its revenues could use data mining to find out if reducing the minimum payments would allow them to earn more interest. If the company has detailed information related to their customers, they should be able to make accurate predictions about how customers will react to policies.