RSSI改进算法下多目标文本数据关联特征定位研究

Research on the Multi-objective Text Data Association Feature Location Based on the RSSI Improvement Algorithm

为了提高文本数据的准确挖掘能力,提出基于RSSI改进算法下的多目标文本数据关联特征定位方法.构建多目标文本数据的关联结构分布模型,采用模糊关联规则匹配方法进行多目标文本数据的特征匹配和语义相关性检测,提取多目标文本数据的语义模糊性定位信息,采用RSSI改进算法进行多目标文本数据关联特征寻优,采用相关性检测技术进行多目标文本数据的集成滤波,结合模糊聚类方法进行多目标文本数据特征分类处理,根据分类结果实现RSSI改进算法下多目标文本数据关联特征定位和挖掘.仿真结果表明,采用该方法进行多目标文本数据关联特征定位的准确性较高,特征匹配能力较强,提高了文本数据挖掘的准确率.

A multi-target text data association feature location method based on improved RSSI algorithm is proposed in order to improve the accurate mining ability of text data.The association structure distribution model of multi-target text data was constructed.The fuzzy association rule matching method was used to detect the feature matching and semantic correlation of multi-target text data,and the semantic fuzziness location information of multi-target text data was extracted.RSSI improved algorithm was used to optimize multi-target text data association features,correlation detection technology was used to integrate filtering multi-target text data,and fuzzy clustering method was used to classify multi-target text data features.According to the classification results,multi-object text data association feature location and mining based on RSSI improved algorithm was realized.The simulation results show that this method has high accuracy and ability of feature matching for multi-target text data association,and improves the accuracy of text data mining.