贝叶斯分类在带优先项的区间编码时态挖掘中的应用
An Application of Bayesian classification to Interval Encoded Temporal mining with prioritized items
摘要：
在现实生活中，媒体信息具有时间属性，或者隐式地或者显式地称为时态数据。本文研究了应用贝叶斯分类的有用性的时间间隔编码的数据库与优先项目。该方法通过对数据库进行编码来进行时态挖掘，该数据库具有加权项，该加权项从用户的角度根据其重要性对项进行优先级排序。朴素贝叶斯分类有助于使产生的时间规则更有效。所提出的基于优先级的时态挖掘（PBTM）方法添加了分类辅助解决问题，在一个明智的和系统的方式。在电信系统投诉数据库中的实验结果表明了该方法的可行性。
英文摘要：
In real life, media information has time attributes either implicitly or explicitly known as temporal data. This paper investigates the usefulness of applying Bayesian classification to an interval encoded temporal database with prioritized items. The proposed method performs temporal mining by encoding the database with weighted items which prioritizes the items according to their importance from the user perspective. Naive Bayesian classification helps in making the resulting temporal rules more effective. The proposed priority based temporal mining (PBTM) method added with classification aids in solving problems in a well informed and systematic manner. The experimental results are obtained from the complaints database of the telecommunications system, which shows the feasibility of this method of classification based temporal mining.
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