Data Mining Overview (also)

Data
How do data mining tools accurately tell you important information that is hidden in the depths of the database? And how do they make predictions? The answer is modeling. Built
Modulo is actually creating a model when you know the results and applying the model to situations that you don't know about. For example, if you
If you want to find an old Spanish shipwreck in the sea, perhaps the first thing you can think of is looking for the time and place where you found the treasure in the past. That
Well, after a survey you found that most of these shipwrecks were found in the Bermuda Sea area, and that the sea zone had some sort of characteristic currents, and the course of that time.
There are certain characteristics to be found. In these many similar features, you abstract and generalize them into a universal model. With this model, you have a good chance of
An unknown treasure was found at another location with a lot of the same characteristics.

Of course, this method of modeling abstraction has been widely used by people before the advent of data mining techniques and even computers. Modeling in the computer and
The previous modeling approach was not very different, and the main difference was that the amount of information the computer could handle was much larger than it used to be. The computer can store known closure
The large number of different cases, and then by the data mining tools from the large amount of information inside the Bisha, will be able to generate models of information extracted. When a model is built
Once it is done, it can be applied to judgments that are similar but whose results are unknown. For example, now suppose you're a marketing director for a telecoms company
Want to develop some new long-distance phone users, then you will be aimlessly to the street to distribute ads? --like wandering aimlessly in the sea to find a treasure
Sample. In fact, it's much more efficient to use your previous business experience to reach out to a customer purposefully than to advertise aimlessly.

As a marketing director, you can know a lot about your customers: Age, sex, credit history, and the use of long distance calls. From
On the good side, mastering the information of these customers is the same information that many potential users have. The problem is you don't necessarily know them.
Long-distance telephone usage (since their long-distance call may be another telecoms company through). Now your main focus is on the users who have more
Long-distance telephone. From the table below, we can abstract some variables from the database and build a model that can be classified and marketed.
Customer potential
General Information
(e.g. demographic data) known
Private information
(e.g. customer transactions) known to be pending

Table Ii. Data Mining applied to classified marketing

Based on the computational model we have created from general information to private information, we can draw information from the table in the lower right table. For example, a telecoms company's
Simplified model can be: more than 60,000 U.S. dollars more than 98% of customers, a monthly long-distance fee of more than 80 dollars. Based on this model, we can use this data to push
Break out of the company is not yet clear private information, so that the new customer base can be generally determined. Market-test data for small markets for such a model to
Said to be extremely useful. Because the small scope of the test data mining, can be the whole market for classified sales to lay a good foundation. Table III describes another
General application of sample Data mining: prediction.
The past is now the future
Static information and current schedule known known known
Dynamic information known to be pending

Table III. Application of data mining in forecasting

The architecture of data mining

Many of the existing data mining tools are independent of the data warehouse, and they require independent input and output data, as well as relatively independent data analysis. For
To maximize the potential of data mining tools, they must be closely integrated with the Data warehouse, like many business analytics software. In this way, people
When the parameters and the depth of analysis are changed, high integration can greatly simplify the data mining process. The following illustration shows a high-level analysis in a large database
Ride.




The Integrated Data Mining system

Using data mining technology, the ideal starting point is to start from a data warehouse, this data warehouse should keep all the customer's contract information, and
and the corresponding market competitor's relevant data. Such a database could be a database of various markets: Sybase, Oracle, redbrick, and its
He and so on, and can be in the data for the speed and flexibility of optimization.

OLAP servers on-line analytical systems can enable a very complex end-user business model to be applied to the data warehouse. The multidimensional structure of a database allows users
Analyze and observe their business operations from different perspectives, such as product classification, geographic classification, or other critical perspectives. Data Mining Server
In this case, the online Analysis server and the data warehouse must be tightly integrated so that the data can be tracked directly and the user is assisted to quickly make the business
Industry decisions, and users can continue to discover better behavior patterns as they update data and apply them to future decisions.

The appearance of the data mining system represents the transformation of the basic structure of the conventional decision support system. Unlike query and reporting languages, you simply feed data query results to
As end users, the data mining Advanced Analysis Server applies the user's business model directly to its data warehouse and feeds the user with a relevant information
Analysis results. This result is an analytic and abstract dynamic view layer that usually varies according to the user's different needs. Based on this view, various reports
Tools and visualization tools can present the results of the analysis to users to help them plan what action to take.

A tool for generating profits

Many companies have successfully installed data mining tools. Most of the companies that used the technology earlier were information-intensive companies such as financial services and
e-mail Marketing system, but now this technology is ready to apply to companies, as long as the company has a large database, and there is strong through the software technology
desire to improve the management of the company. But using data mining technology, the company must be two key factors, one is a large, integrated database;
is a well-defined business process in which data mining is applied closely to company data.

Some successful applications of data mining techniques, such as a pharmaceutical company, determine which of its most recent marketing strengths and sales results are analyzed
Marketing campaigns have had the greatest impact on the high value-added physician community in recent months, based on a competitor's sales activity information and local health status.
Data Systems. The drug company can then communicate the results of the analysis to the local sales representatives through its office network, and the sales representatives are able to
Division to deliver the key information to make the appropriate sales decisions, so that in fast changing, dynamic market, sales representatives can be based on a variety of special circumstances
Analysis to make the best choice.

Conclusion
The comprehensive integration of customers, suppliers and market information of large data warehouses led to explosive growth of information within the company, enterprises in the market competition, the need and
This information is sometimes used to make complex analysis. In order to more timely and more accurately make the choice for the enterprise, based on the relational database and online analysis
Technical data mining tools have brought us a new turnaround. Currently, data mining tools are evolving at an unprecedented rate and expanding the user base
Body, in the increasingly fierce market competition in the future, with data mining technology will be faster than others to obtain more rapid response, to win more business opportunities.