基于实验设计的电信监控数据挖掘工具评价

Samneet Singh, Yan Liu, Wayne Ding, Zheng Li
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引用次数: 6

摘要

电信监控数据要求数据分析工作流程的自动化。数据挖掘工具提供数据工作流管理系统来处理和执行分析任务。本文根据实验设计原则(DOE)对两个示例数据挖掘工具进行了评估,以运行电信监控数据的预测和聚类工作流。我们对从试验移动网络收集的数据集进行了定量和定性评估。这些数据集包括1个月、6个月、1年和2年的时间框架,提供了基站上每个小区连接用户的平均数量。该评估的观察结果提供了对数据分析工作流程上下文中每个数据挖掘工具的见解。该实验设计文件将进一步促进本评估研究的复制和评估其他数据挖掘工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Data Mining Tools for Telecommunication Monitoring Data Using Design of Experiment
Telecommunication monitoring data requires the automation of data analysis workflows. A data mining tool provides data workflow management systems to process and perform analysis tasks. This paper presents an evaluation of two example data mining tools following the principles of design of experiment (DOE) to run forecasting and clustering workflows for telecom monitoring data. We conduct both quantitative and qualitative evaluation on datasets collected from a trial mobile network. The datasets consist of 1 month, six months, one year and two years of time frames that provide the average number of connected users per cell on base stations. The observations from this evaluation provide insights of each data mining tool in the context of data analysis workflows. This documented design of experiment will further facilitate replicating this evaluation study and evaluate other data mining tools.
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