数据挖掘常用分类算法研究

Data Mining Classification Algorithm

数据库、数据仓库以及其他存储信息库中潜藏着很多与商业、科学研究等活动的决策有关的数据和知识。对于数据挖掘中的数据分析，通常有两种常见的方法，即分类和预测，首先对数据库中的数据进行分类归纳，然后根据分类规则可以得到比较有价值的数据，然后我们可以根据这个数据来预测得到一些包含未来趋势的信息。在常见的分类算法中，决策树算法是一个有着很好扩展性的算法，可以应用到大型数据库中，可以对多种数据类型进行处理，分类模式容易转化为分类规则，结果也十分的浅显易懂易于理解。该文主要先介绍了几种常用的分类算法，然后具体介绍决策树算法的过程以及在分类算法实际应用中的优缺点。

Databases, data warehouses and other storage repository with a lot of potential commercial, scientific research and other activities related to the decision-making data and knowledge. For data analysis, data mining, there are usually two common meth-ods, ie, classification and prediction, the first data in the database were classified summarized, then you can get more valuable data in accordance with the classification rules, then we can based on this data Some information contained predicted future trends. In common classification algorithms, decision tree algorithm is an algorithm has good scalability, can be applied to large databases, can handle a variety of data types, classification mode easily converted into classification rules, the results are very plain and easy understand easily understood. This paper first introduces several commonly used classification algorithms, and then introduced the process of decision tree algorithm and the advantages and disadvantages in practical application of classification algorithms.