Abstract: Aiming at telecom insolvency mining，combining with the imbalance nature of telecom insolvency data，the research priority is set upon the impact on classification result caused by missing values and outliers，and thus a Data Quality Assessment System for Telecom Insolvency Mining（TIM-DQAS） is presented.In the missing evaluation sub-system，a class-
distribution-based attribute weighting algorithm is presented to measure the missing costs of input attributes.In the outlier evaluation sub-system，the impact on classification result caused by outliers in imbalance data is analyzed，and the outlier degree is proposed to measure the impact caused by outliers.Based on a series of contrast experiments on telecom personal handphone data of a city，a reference assessing result is provided，and the effectiveness of the assessing strategy is verified.

Key words: telecom, data mining, insolvency, data quality assessment, missing value, imbalance, outlier degree

摘要： 针对电信欠费挖掘主题，结合电信欠费数据非平衡的特点，重点研究了缺失与离群数据对分类结果的影响，从而提出了一个面向电信欠费挖掘的数据质量评估体系（TIM-DQAS）：对于缺失评估，提出了一种基于类分布差异的属性加权算法，以衡量输入属性的缺失代价；对于离群评估，分析了非平衡数据中的离群点对分类结果的影响，提出离群度的概念，以量化离群点的影响。基于某城市电信小灵通数据的对比实验，给出了评估结果的参照值，验证了评估策略的有效性。

关键词: 电信, 数据挖掘, 欠费主题, 数据质量评估, 缺失, 非平衡, 离群度, ,