数据挖掘(Data Mining)阶段首先要确定挖掘的任务或目的。数据挖掘的目的就是得出隐藏在数据中的有价值的信息。数据挖掘是一门涉及面很广的交叉学科，包括器学习、数理统计、神经网络、数据库、模式识别、粗糙集、模糊数学等相关技术。它也常被称为“知识发现”。知识发现(KDD)被认为是从数据中发现有用知识的整个过程。数据挖掘被认为是KDD过程中的一个特定步骤，它用专门算法从数据中抽取模式(patter，如数据分类、聚类、关联规则发现或序列模式发现等。数据挖掘主要步骤是：数据准备、数据挖掘、结果的解释评估。-Data Mining (Data Mining) stage must first determine the mission or purpose of the excavation. The purpose of data mining is to draw valuable information hidden in the data. Data mining is an interdisciplinary involving a wide range, including control study, mathematical statistics, neural networks, databases, pattern recognition, rough sets, fuzzy mathematics and other related technologies. It is also often referred to as the " knowledge discovery" . Knowledge discovery (KDD) is that the whole process is to discover useful knowledge from data. Data mining is a particular step in the KDD process, with a special algorithm (patter, such as data classification, clustering, association rules discovery or sequential pattern discovery. Extracted from the data model and data mining major steps: data preparation, data mining, interpretation of the results evaluated.