Since 1986. a brand-new modern industrial city, full of vigor and vitality, has been towering aloftrapidly in what used to be salt marsh in the western part of Bohai Bay of China. With an annual out-put value of RMB ?...Since 1986. a brand-new modern industrial city, full of vigor and vitality, has been towering aloftrapidly in what used to be salt marsh in the western part of Bohai Bay of China. With an annual out-put value of RMB ? 14 billion. it has become one of the most lively and energetic areas of Tianjin andeven throughout the country for opening to the outside world and for developing exportoriented econo-展开更多
Ⅰ. The present Situation of Transnational Corporations’ Investment in TEDA (1 ) General situation Since its founding ten years ago, especially since 1992. TEDA has achieved encouraging successesin undertaking major ...Ⅰ. The present Situation of Transnational Corporations’ Investment in TEDA (1 ) General situation Since its founding ten years ago, especially since 1992. TEDA has achieved encouraging successesin undertaking major projects. drawing the attention of leading consortia, omnibearingly solicitingbusiness and drawing foreign capital. By September 30, 1994, TEDA had approved 2054 solely for-展开更多
Since its establishment about ten years ago, TEDA has taken advantage of eachopportune time created by China’s policy of opening wider and wider to the outsideworld and the rapid economic growth to deepen its reform ...Since its establishment about ten years ago, TEDA has taken advantage of eachopportune time created by China’s policy of opening wider and wider to the outsideworld and the rapid economic growth to deepen its reform and extend its exploitation,and thus has maintained a favorable momentum and an extraordinary high speed foreconomic development. With the total economic output of the whole area raised to展开更多
Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims...Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.展开更多
文摘Since 1986. a brand-new modern industrial city, full of vigor and vitality, has been towering aloftrapidly in what used to be salt marsh in the western part of Bohai Bay of China. With an annual out-put value of RMB ? 14 billion. it has become one of the most lively and energetic areas of Tianjin andeven throughout the country for opening to the outside world and for developing exportoriented econo-
文摘Ⅰ. The present Situation of Transnational Corporations’ Investment in TEDA (1 ) General situation Since its founding ten years ago, especially since 1992. TEDA has achieved encouraging successesin undertaking major projects. drawing the attention of leading consortia, omnibearingly solicitingbusiness and drawing foreign capital. By September 30, 1994, TEDA had approved 2054 solely for-
文摘Since its establishment about ten years ago, TEDA has taken advantage of eachopportune time created by China’s policy of opening wider and wider to the outsideworld and the rapid economic growth to deepen its reform and extend its exploitation,and thus has maintained a favorable momentum and an extraordinary high speed foreconomic development. With the total economic output of the whole area raised to
基金This research was funded by the National Natural Science Foundation of China(Grant No.72001190)by the Ministry of Education’s Humanities and Social Science Project via the China Ministry of Education(Grant No.20YJC630173)by Zhejiang A&F University(Grant No.2022LFR062).
文摘Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research.
