Post-hoc cluster analysis of connection between the forming characteristics

Dronov, S. V.; Evdokimov, Eugeny A.

doi:10.3233/mas-180429

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Similar to the general cluster innovation network, the relationship connection between the subjects include both formal cooperation agreements and informal exchanges and communication. With the increasing of relationships between the subjects, the connection can be strong or weak (Dronov;Evdokimov, 2018). In addition to the relationship connection of the industrial chain model, there is also a strong competition-cooperation model relationship connection between cluster enterprises.…”

Section: Evolution Mechanism and Empirical Analysismentioning

confidence: 99%

Evolution mechanism and empirical analysis of innovation network in advanced manufacturing industry

et al. 2022

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As the “chaotic” edge of innovation system has strong innovation potential and is easy to form and develop the emerging technology innovation network, the formation process of emerging technologies and their relationship with the development trajectory of original technologies are analyzed, and the evolution mechanism of innovation network of advanced manufacturing industry is deeply studied combined with life cycle theory. Firstly, empirical analysis is carried out by collecting patents data in the industrial robotics field. Then, the IPC co-occurrence network and patentee citation network are plotted by combining patents citation analysis with social network analysis. Next, the technical characteristics and knowledge flow characteristics of an advanced manufacturing innovation network are verified by calculating various indicators of the network. Finally, the empirical results show that the technology structure in the field of industrial robotics has high heterogeneity, wide integration among technical fields, and knowledge flow network has a small-world effect, characterized by easy flow, wide flow direction, high efficiency, fuzzy network boundary, and numerous and diversified core the key players in innovation.

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Section: Evolution Mechanism and Empirical Analysismentioning

confidence: 99%

Evolution mechanism and empirical analysis of innovation network in advanced manufacturing industry

et al. 2022

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“…Кластерный анализ применяется в различных областях [4][5][6][7][8]. Технологии кластеризации исключительно полезны, когда нужно проанализировать структуру массивов больших данных непосредственно либо как вспомогательные методы при снижении размерности данных.…”

Section: вводные замечанияunclassified

“…Например, возможны кластеры удлиненной формы либо ситуация, когда кластеры представлены длинными «цепочками» и т.д., а некоторые методы могут создавать кластеры произвольной формы. Кроме того, результат кластеризации одним и тем же методом может изменяться в зависимости от выбранной метрики расстояния либо процедуры стандартизации данных [1][2][3][4].…”

Section: вводные замечанияunclassified

Simplified Silhouette Parameter for Assessing the Quality of Cluster Structures

Zhuravleva¹,

Manicheva²

2022

Известия АлтГУ

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The article deals with issues related to assessing the quality of a cluster data structure. A description of the clustering quality index is given, which takes into account the characteristics of compactness and separability of clusters in two versions: the classical and the simplified silhouette index. It is noted that a laborious procedure of a complete enumeration of pairs of objects is required to evaluate the classical silhouette feature on big data. Further, a variation of this indicator — a simplified silhouette indicator — is proposed and found to be convenient for assessing cluster structures built on big data arrays. The sample indicator has been tested on model data, and several variants of cluster structures are built for the objects like identified clusters that are present in the set of mini-clusters. The centers of mini-clusters with consideration to their “weight” (the number of objects in mini-clusters was set as the weight) are chosen as objects when calculating intracluster and inter-cluster distances. The corresponding silhouette parameter is calculated. The comparison of the indicators of the classical and simplified silhouette indicators for each set of data models provides an adequate assessment of the quality of clustering.

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“…Cluster Innovation Network. Similar to the general cluster innovation network, the relationship connection between the subjects includes both formal cooperation agreements and informal exchanges and communication, and with the number of relationships between the subjects, the connection can be strong or weak [14]. Since the difference between equipment manufacturing clusters and general industrial clusters is mainly reflected in the enterprise nodes, this section focuses on the relationship between enterprises and enterprises.…”

Section: Relational Connection Of Equipment Manufacturingmentioning

confidence: 99%

Evolution Mechanism of Advanced Equipment Manufacturing Innovation Network Structure from the Perspective of Complex System

Wang

Xing

2021

Complexity

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Our country’s equipment manufacturing industry ranks among the best in all developing countries, but compared with developed countries, there is still a long way to go. It is not only the backwardness of various technologies, but also the interference of other countries. Although our country's equipment manufacturing industry is not as advanced as the advanced technology of developed countries, we still have to stick to our original aspirations, do not underestimate ourselves, and be good at absorbing and learning from the strengths of others to make up for our own weaknesses. While not working behind closed doors and while absorbing technology from other countries, we can make use of our strengths to make up for our weaknesses and develop our own industrial technology. This paper studies the evolution trend of innovation network structure and at the same time studies the evolution mechanism of advanced equipment manufacturing innovation network structure from the perspective of complex systems. The explained variable in this article is green total factor productivity. The variable adopts the Malmquist–Luenberger global super-efficiency index model. There are two main explanatory variables. One is the heterogeneity that affects the efficiency of industrial evolution, including factor heterogeneity, structural heterogeneity, and environmental heterogeneity, and the other is the interaction term of equipment manufacturing specialization agglomeration degree dummy variable multiplied by factor heterogeneity. The regional economic development level is added to the model as a control variable. In the selection of measurement indicators, the per capita GDP is used as the control variable. The experimental results show that each sample is tested in pairs, and the standard error level of the mean is 0.018, which is less than 0.05, indicating that the efficiency of the equipment manufacturing industry’s economic correlation spatial network has a significant impact on the overall economic development level of the industry. The reduction in spur helps to increase economic output.

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Post-hoc cluster analysis of connection between the forming characteristics

Cited by 4 publications

References 2 publications

Evolution mechanism and empirical analysis of innovation network in advanced manufacturing industry

Evolution mechanism and empirical analysis of innovation network in advanced manufacturing industry

Simplified Silhouette Parameter for Assessing the Quality of Cluster Structures

Evolution Mechanism of Advanced Equipment Manufacturing Innovation Network Structure from the Perspective of Complex System

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