由于PPI网络数据的无尺度和小世界特性,使得目前对此类数据的聚类算法效果不理想.根据PPI网络的拓扑结构特性,本文提出了一种基于连接强度的蚁群优化(Joint Strength based Ant Colony Optimization,JSACO)聚类算法,该算法引入了连接强...由于PPI网络数据的无尺度和小世界特性,使得目前对此类数据的聚类算法效果不理想.根据PPI网络的拓扑结构特性,本文提出了一种基于连接强度的蚁群优化(Joint Strength based Ant Colony Optimization,JSACO)聚类算法,该算法引入了连接强度的概念对蚁群聚类算法中的拾起/放下规则加以改进,以连接强度作为拾起规则,对结点进行聚类,并根据放下规则放弃部分不良数据,产生最终聚类结果.最后采用了MIPS数据库中的PPI数据进行实验,将JSACO算法与PPI网络数据的其他聚类算法进行比较,聚类结果表明JSACO算法正确率高,时间开销低.展开更多
This paper focuses on the demonstration study, through related analysis on various samples, on the relation between PPI and CPI, based on the resident consumption price index and the price index of industrial products...This paper focuses on the demonstration study, through related analysis on various samples, on the relation between PPI and CPI, based on the resident consumption price index and the price index of industrial products in Shandong province since 1989. The paper also analyzes the relation among different price index’s, their transfer regulation and the degree of influence on one another. The study result shows that resident consumption price index relates the most to the factory price index of daily living products, the second to the factory price index of production materials, and the least to the purchase price index of raw materials, fuel and power. The price change of energy and raw materials at the upper level will naturally be reflected in the factory price of industrial products at the lower level and price of daily living products in the end. It is a long influence process from the price of raw materials for industrial production to the factory price of industrial products, including production materials and daily living products, and finally to the resident consumption price. The price influence process somehow lags behind the production and consumption process for a certain period. The rise and fall of industrial products price index is the earliest signal for economic development trend and to which the government and the industry must attach high importance.展开更多
文摘由于PPI网络数据的无尺度和小世界特性,使得目前对此类数据的聚类算法效果不理想.根据PPI网络的拓扑结构特性,本文提出了一种基于连接强度的蚁群优化(Joint Strength based Ant Colony Optimization,JSACO)聚类算法,该算法引入了连接强度的概念对蚁群聚类算法中的拾起/放下规则加以改进,以连接强度作为拾起规则,对结点进行聚类,并根据放下规则放弃部分不良数据,产生最终聚类结果.最后采用了MIPS数据库中的PPI数据进行实验,将JSACO算法与PPI网络数据的其他聚类算法进行比较,聚类结果表明JSACO算法正确率高,时间开销低.
文摘This paper focuses on the demonstration study, through related analysis on various samples, on the relation between PPI and CPI, based on the resident consumption price index and the price index of industrial products in Shandong province since 1989. The paper also analyzes the relation among different price index’s, their transfer regulation and the degree of influence on one another. The study result shows that resident consumption price index relates the most to the factory price index of daily living products, the second to the factory price index of production materials, and the least to the purchase price index of raw materials, fuel and power. The price change of energy and raw materials at the upper level will naturally be reflected in the factory price of industrial products at the lower level and price of daily living products in the end. It is a long influence process from the price of raw materials for industrial production to the factory price of industrial products, including production materials and daily living products, and finally to the resident consumption price. The price influence process somehow lags behind the production and consumption process for a certain period. The rise and fall of industrial products price index is the earliest signal for economic development trend and to which the government and the industry must attach high importance.
