A fast prototype reduction method based on template reduction and visualization-induced self-organizing map for nearest neighbor algorithm

2013 ◽  
Vol 39 (3) ◽  
pp. 564-582 ◽  
Author(s):  
I-Jing Li ◽  
Jia-Chian Chen ◽  
Jiunn-Lin Wu
Forests ◽  
2014 ◽  
Vol 5 (7) ◽  
pp. 1635-1652 ◽  
Author(s):  
Leonhard Suchenwirth ◽  
Wolfgang Stümer ◽  
Tobias Schmidt ◽  
Michael Förster ◽  
Birgit Kleinschmit

Biology ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 598
Author(s):  
Do-Hun Lee ◽  
Nam Jung ◽  
Yong-Hyeok Jang ◽  
KyoungEun Lee ◽  
Joobaek Lim ◽  
...  

Nutrias (Myocastor coypus) were imported to South Korea for farming in 1985; individuals escaped captivity and established wild populations in natural ecosystems in the late 1990s. Numerous studies have focused on their monitoring and management; however, information on the continuous movement of individuals is not available. In this study, telemetry data from field conditions were used to identify the nearest-neighbor distances of individuals in association with environmental factors, including plant type, land cover, and biological parameters. The minimum nearest-neighbor distances for the different sexes were, overall, according to the minimum distances for the same sex. Local co-occurrences of individuals, either of the same or different sex, were seasonal. Tall grasslands, followed by herbaceous vegetation, were associated with the co-occurrence of different sexes. Conversely, floating-leaved hydrophytes, followed by xeric herbaceous vegetation, were correlated with the co-occurrence of the same sex. Local female–male co-occurrences were negatively associated with male–male co-occurrences but not with female–female co-occurrences, suggesting male dominance in group formations. Movement and co-occurrence information extracted using Geo-self-organizing maps furthers our understanding of population dispersal and helps formulate management strategies for nutria populations.


Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn't restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.


Author(s):  
XIAOLIAN GUO ◽  
HAIYING WANG ◽  
DAVID H. GLASS

The Bayesian self-organizing map (BSOM) has typically been used for density estimation. In this study, we implemented an adaptation of the model for performing unsupervized and supervised classification. In order to determine the optimal number of neurons to represent the given dataset during the learning process, an extended Bayesian learning process is proposed called the growing BSOM. It starts with two neurons and adds new neurons to the network via a process in which the neuron with the lowest individual log-likelihood is identified. The system has been tested using three synthetic datasets and one real dataset. The experimental results suggest that the BSOM-based approach can achieve better classification performance in comparisons to several widely-used models such as k-nearest neighbor (KNN), support vector machine (SVM) and Gaussian mixture model (GMM). By using the Bayesian information criterion (BIC) as a stopping criterion, the growing BSOM can model the data under study and estimate the number of clusters.


2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato

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