Research on Optimized Clustering Analysis Algorithm

2013 ◽  
Vol 811 ◽  
pp. 407-412
Author(s):  
Lin Tong ◽  
Di Wu

In this paper an improved clustering analysis algorithm is proposed on the basis of randomly selected data in the CURE algorithm and the cluster centers setting in the K-NN algorithm. The combination of the two algorithms conquers the poor clustering accuracy in the CURE algorithm and the clustering deficiency on large data set in the K-NN algorithm. This paper first introduces the concept of clustering analysis and its real-life applications, then proceeds to describe the method of clustering analysis, and then introduces optimized CURE_KNN algorithm described in pseudo-code, at last the advantages of the algorithm are summarized.

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson

2014 ◽  
Author(s):  
Carlos Enrique Gutierrez ◽  
Prof. Mohamad Reza Alsharif ◽  
Mahdi Khosravy ◽  
Prof. Katsumi Yamashita ◽  
Prof. Hayao Miyagi ◽  
...  

2011 ◽  
Vol 46 (4) ◽  
pp. 943-966 ◽  
Author(s):  
Venky Nagar ◽  
Kathy Petroni ◽  
Daniel Wolfenzon

AbstractA major governance problem in closely held corporations is the majority shareholders’ expropriation of minority shareholders. As a solution, legal and finance research recommends that the main shareholder surrender some control to minority shareholders via ownership rights. We test this proposition on a large data set of closely held corporations. We find that shared-ownership firms report a substantially larger return on assets and lower expense-to-sales ratios. These findings are robust to institutionally motivated corrections for endogeneity of ownership structure. We provide evidence on the presence of governance problems and the effectiveness of shared ownership as a solution in settings characterized by illiquidity of ownership.


Author(s):  
Marcos Rodrigues Saude ◽  
Marcelo de Medeiros Soares ◽  
Henrique Gomes Basoni ◽  
Patrick Marques Ciarelli ◽  
Elias Oliveira
Keyword(s):  
Data Set ◽  

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