scholarly journals An Improved Clustering Method with Cluster Density Independence

2015 ◽  
Vol 20 (12) ◽  
pp. 15-20 ◽  
Author(s):  
Byeong-Hyeon Yoo ◽  
Wan-Woo Kim ◽  
Gyeongyong Heo
Keyword(s):  
Clustering Method ◽  
Density Independence ◽  
Cluster Density

scholarly journals Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Muchamad Taufiq Anwar ◽  
Wiwien Hadikurniawati ◽  
Edy Winarno ◽  
Aji Supriyanto
Keyword(s):  
Risk Analysis ◽  
Historical Data ◽  
Risk Map ◽  
Clustering Method ◽  
Wildfire Risk ◽  
Dbscan Clustering ◽  
Cluster Density ◽  
Density Evaluation

Wildfire risk analysis can be based on historical data of fire hotspot occurrence. Traditional wildfire risk analyses often rely on the use of administrative or grid polygons which has their own limitations. This research aims to develop a wildfire risk map by implementing DBSCAN clustering method to identify areas with wildfire risk based on historical data of wildfire hotspot occurrence points. The risk ranks for each area/cluster were then ranked/calculated based on the cluster density. The result showed that this method is capable of detecting major clusters/areas with their respective wildfire risk and that the majority of consequent fire occurrences were repeated inside the identified clusters/areas.Keywords: wildfire risk map; clustering; DBSCAN; cluster density;

Formation Mechanisms for Multiply Twinned Particles During Nucleation in the Au/MICA System

1975 ◽  
Vol 33 ◽  
pp. 84-85
Author(s):  
Eal H. Lee ◽  
Helmut Poppa
Keyword(s):  
Deposition Time ◽  
High Vacuum ◽  
Dark Field ◽  
Ultra High Vacuum ◽  
Formation Mechanisms ◽  
Heat Treated ◽  
Cluster Density ◽  
Diffraction Patterns ◽  
Crystal Particle ◽  
Multiply Twinned Particles

The formation of thin films of gold on mica has been studied in ultra-high vacuum (5xl0-10 torr) . The mica substrates were heat-treated for 24 hours at 375°C, cleaved, and annealed for 15 minutes at the deposition temperature of 300°C prior to deposition. An impingement flux of 3x1013 atoms cm-2 sec-1 was used. These conditions were found to give high number densities of multiple twin particles and are based on a systematic series of nucleation experiments described elsewhere. Individual deposits of varying deposition time were made and examined by bright and dark field TEM after "cleavage preparation" of highly transparent specimens. In the early stages of growth, the films generally consist of small particles which are either single crystals or multiply twinned; a strong preference for multiply twinned particles was found whenever the particle number densities were high. Fig. 1 shows the stable cluster density ns and the variation with deposition time of multiple twin particle and single crystal particle densities, respectively. Corresponding micrographs and diffraction patterns are shown in Fig. 2.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

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