Application Of Data Mining Technology In The Inner Mongolia Autonomous Region Population Data

Data mining techniques in computer information processing technology is very popular in recent years, and this technology knowledge extraction achieved very good results in large-scale data. Data mining technology has been widely used, applied to the industry usually play the role of guidance and decision-making.A lot of data mining methods can solve the different data of the problem to be solved by a different method, you can also get different forms of knowledge. The use of decision tree classification of mass data mining is the current popular method is efficient and accurate decision tree technology characteristics. Rules through the decision tree can easily extracted, and converted into a knowledge.The population data is critical for a country in terms of population core of the country’s most. Population data along with the development of information technology and database technology gradually sound and large, how to make better use of the formulation of national and regional policies of population data services, and ultimately serve the social and demographic already is an urgent need of modern society.This paper studies how the decision tree classification techniques of data mining, knowledge discovery in the Inner Mongolia Autonomous Region population data, the main concern is the potential classification techniques found between demographic attributes Contact. In a more in-depth introduction to data mining and decision theory. Selected population of a certain area of the Inner Mongolia Autonomous Region in the practical application as the object of study, data reduction, integration and simplify the use of pretreatment technology, thus in line with the data requirements of data mining. Next, based on information gain and excellent rate of information gain-based decision tree algorithm generates a decision tree. Then optimize the decision tree generated in accordance with the characteristics of the Inner Mongolia Autonomous Region population data to improve the accuracy of data mining. Next, using the pessimistic error rate pruning optimized decision tree size control, thus facilitating the extraction of knowledge. Finally, the use of "IF-THEN" rules were rules extraction, extraction rules stored in the Knowledge Base through a graphical interface, the user can obtain the required knowledge of the paper, according to the theory of demographic knowledge extracted briefly analysis, the required inter-related reasons for the population attribute.Large-scale data cannot be obtained through a simple query statistics-depth knowledge of the data, the huge data will only go to waste With data mining theory various data mining methods in-depth analysis of the data from the research can be found can get between the number of potential knowledge,of course, data mining is not laissez-faire attitude to carry out excavation, require flexible use of algorithms based on the needs of users and the characteristics of the data to get more accurate knowledge and rich.