A Fuzzy Semisupervised Clustering Method: Application to the Classification of Scientific Publications

2014 ◽  
pp. 179-188 ◽  
Author(s):  
Irene Diaz-Valenzuela ◽  
Maria J. Martin-Bautista ◽  
Maria-Amparo Vila
Keyword(s):  
Clustering Method ◽  
Scientific Publications ◽  
Semisupervised Clustering

OPPORTUNITIES FOR THE DEVELOPMENT OF MILITARY TOURISM IN UKRAINE

2018 ◽  
Author(s):  
Mariya Lushchyk ◽  
◽  
Yaroslava Moskvyak ◽  
Keyword(s):  
Young People ◽  
Basic Research ◽  
Military History ◽  
Theoretical Studies ◽  
Scientific Publications ◽  
Military Life ◽  
Historical Reconstructions ◽  
Constant Interest

The constant interest of people in military history, historical battles, battles and other military heritage promotes military tourism. However, despite the presence of a certain array of research and publications on various aspects of the development of this type of tourism, basic research in terms of defining and classifying types of military tourism, the geography of military tourism in Ukraine is not identified. Therefore, theoretical studies of the essence of military tourism, as well as the potential and opportunities for the development of this type of tourism in Ukraine are timely and relevant. To achieve this goal, an analysis of the main theoretical provisions and principles of military tourism, covered in domestic and foreign scientific publications. It is proposed to understand military tourism as one of the types of tourism, which involves visiting historical and modern military sites / institutions / locations, attracting tourists to the realities of modern military life, in safe military events or their historical reconstructions, etc. Among the tasks of military trism, its importance is clarified, first of all, for the patriotic upbringing of young people, especially in modern conditions, when the heroism and love of freedom of ancestors comes to life in Ukrainian soldiers who defend the country from Russian aggression. The main motives of tourists for military travel are described. The typification of the main criteria and principles of classification of military tourism in tourist activity is given. The author's classification of types of military tourism is offered, according to which military-historical, military and military-event types of tourism are distinguished. «Military» places and locations of Ukraine were monitored. This allowed to identify potentially popular for tourists objects: ramparts, fortresses, castles, castles, bastions, forts, defensive monasteries, temples, cathedrals, fortresses, defensive lines, battlefields, battles and military glory, which are associated with feats of national heroes, museums, memorial complexes, monuments dedicated to military themes, dioramas, bunkers, bunkers, DOTs, trenches, remnants of equipment, etc., which can be used in the development of new tourist products of military tourism.

scholarly journals A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4786
Author(s):  
Yanpeng Hao ◽  
Zhaohong Yao ◽  
Junke Wang ◽  
Hao Li ◽  
Ruihai Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Transmission Lines ◽  
Prediction Models ◽  
Power Grids ◽  
Curve Shape ◽  
Clustering Method ◽  
Characteristic Parameters ◽  
Prediction Ability ◽  
Icing Prediction

Icing forecasting for transmission lines is of great significance for anti-icing strategies in power grids, but existing prediction models have some disadvantages such as application limitations, weak generalization, and lack of global prediction ability. To overcome these shortcomings, this paper suggests a new conception about a segmental icing prediction model for transmission lines in which the classification of icing process plays a crucial role. In order to obtain the classification, a hierarchical K-means clustering method is utilized and 11 characteristic parameters are proposed. Based on this method, 97 icing processes derived from the Icing Monitoring System in China Southern Power Grid are clustered into six categories according to their curve shape and the abstracted icing evolution curves are drawn based on the clustering centroid. Results show that the processes of ice events are probably different and the icing process can be considered as a combination of several segments and nodes, which reinforce the suggested conception of the segmental icing prediction model. Based on monitoring data and clustering, the obtained types of icing evolution are more comprehensive and specific, and the work lays the foundation for the model construction and contributes to other fields.

Indonesian Mutual Funds Classification Using Clustering Method

2015 ◽  
Vol 21 (4) ◽  
pp. 826-829
Author(s):  
Ir. Dewi Tamara ◽  
Shintia Revina
Keyword(s):  
Mutual Funds ◽  
Mutual Fund ◽  
Money Market ◽  
Fixed Income ◽  
Clustering Method ◽  
New Classification ◽  
Asset Value ◽  
Net Asset Value ◽  
Alternative Investment

Mutual funds have existed since 1990 as an alternative investment in Indonesia. The objective of this research is to examine the existing classification of mutual funds database. The data of mutual funds is taken from Bloomberg through Portal Reksadana 2013 which covered 690 mutual funds. The existing classification consists of mutual funds fixed income (reksadana pendapatan tetap), equity (reksadana saham), money market (reksadana pasar uang) and structured (reksadana campuran). The existing financial attributes consists of the net asset value, percentage annualized return the last 6 months, 1 year, 3 years, 5 years and year-to-date. This paper uses K-means clustering to propose new classification of Indonesian mutual funds. The result reveals that mutual funds in equity and fixed income belong to its group. However, mutual funds money market is belong to mutual fund fixed income and mutual funds structures are identified to mutual funds equity. Furthermore, we find that in average 43% of Indonesian mutual funds are misclassified in accordance with their attributes. Finally, it is suggested to re-group the mutual funds into smaller classification, which has lower rates of misclassified mutual funds and possibility to achieve better performances in terms of its percentage annualized return.

Topics and Search Based Classification of Scientific Publications

2015 ◽  
Vol 12 (12) ◽  
pp. 5210-5222
Author(s):  
Damien Hanyurwimfura ◽  
Liao Bo ◽  
Chen Xiangtao ◽  
Vincent Havyarimana ◽  
Yan Jiang
Keyword(s):  
Scientific Publications
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