摘要
通过对自组织挖掘算法的理论分析和实验研究,对自组织数据挖掘和灰色理论在不同对象中的拟合和预测效果进行比较分析,揭示了自组织数据挖掘与灰色预测方法的不同特征和属性。灰色数列预测是指利用动态GM模型,对系统的时间序列进行数量大小的预测,即对系统的主行为特征量或某项指标,发展变化到未来特定时刻出现的数值进行预测。比较研究的结果显示:对于周期性的贫信息数据及噪声干扰的复杂系统,灰色理论是适宜的选择。
Based on the theoretical analysis of the algorithm and experiment research, this paper elucidates the distinction and relation between the self-organizing data mining and Grey Theory method. It revealed the different character and attribution for the self-organizing data mining and Grey theory. The grey an ordered series of numbers forecasting is using dynamic GM＇ s model , and the time alignment to the system carries on the forecasting of quantity dimension, and with namelying main action characteristic capacity or certain quota to the system is developed numerical value that changes the emergence to the specially designated or appointed moment of future to calculate. Our research shows that the Grey Theory method is a suitable choice on the modeling and forecasting for poor information system and complex systems with noise.
出处
《数字通信》
2014年第5期10-12,共3页
Digital Communications and Networks
关键词
灰色预测
自组织数据挖掘
GM模型
regression analysis, self-organizing data mining, GM＇ s model
