The Improvement of HMM Algorithm using wavelet de-noising in speech recognition

摘要：

This paper proposes a multi-dimensional time series data mining model for the meteorological data, In this model the dimensions redundant reduction algorithm is used for reducing the redundant dimensions and the complexity of data mining, the extremum slope piecewise linear fitting method is used to implement multi-dimensional meteorological time series segmentation, data compression and feature value extraction, reduce the difficulty of data mining, then use k-means cluster to make the symbols of sequence; final rule extraction is used for getting useful rules in experiments. The results of experiment show that this model has a great practicability.

关键词： Data mining Meteorological factors Multi-dimensional time series

作者: ZHOU Dexiang ZHENG Liping

作者单位: College of Information Science and TechnologyHenan University of Technology Zhengzhou, China College of Information Science and Technology Henan University of Technology Zhengzhou, China

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）