Cluster analysis preprocessor for path loss modelling using neural networks

1995 ◽  
Vol 31 (22) ◽  
pp. 1894-1895 ◽  
Author(s):  
Q. Zhou ◽  
A.K.Y. Lai
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Path Loss ◽  
Loss Modelling

scholarly journals Cluster analysis of knowledge sources in standardized electrical engineering subfields

2016 ◽  
Vol 13 (3) ◽  
pp. 405-422
Author(s):  
Marija Blagojevic ◽  
Zivadin Micic ◽  
Momcilo Vujicic
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Electrical Engineering ◽  
Financial Resources ◽  
Knowledge Sources ◽  
Knowledge Source ◽  
Innovation Intensity ◽  
Future Work ◽  
Analysis Of Knowledge ◽  
Mathematical Relations

The paper presents a cluster analysis of innovation of knowledge sources based on the standards in the field of Electrical Engineering. Both local (SRPS) and global (ISO) knowledge sources have been analysed with the aim of innovating a Knowledge Base (KB). The results presented indicate a means/possibility of grouping the subfields within a cluster. They also point to a trend or intensity of knowledge source innovation for the purpose of innovating the KB that accompanies innovations. The study provides the possibility of predicting necessary financial resources in the forthcoming period by means of original mathematical relations. Furthermore, the cluster analysis facilitates the comparison of the innovation intensity in this and other (sub)fields. Future work relates to the monitoring of the knowledge source innovation by means of KB engineering and improvement of the methodology of prediction using neural networks.

Exploring the Unknown Nature of Data

2010 ◽  
pp. 1-27
Author(s):  
Rui Xu ◽  
Donald C. Wunsch II
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Data Structures ◽  
Clustering Algorithms ◽  
Ground Truth ◽  
Human Beings ◽  
Data Set ◽  
Learning Capabilities ◽  
Good Learning ◽  
Hidden Data

To classify objects based on their features and characteristics is one of the most important and primitive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with little or no prior information, into a finite and discrete set of “natural,” hidden data structures. Here, the authors introduce and discuss clustering algorithms that are related to machine learning and computational intelligence, particularly those based on neural networks. Neural networks are well known for their good learning capabilities, adaptation, ease of implementation, parallelization, speed, and flexibility, and they have demonstrated many successful applications in cluster analysis. The applications of cluster analysis in real world problems are also illustrated. Portions of the chapter are taken from Xu and Wunsch (2008).

Classification of Fourier Transform Infrared Microscopic Imaging Data of Human Breast Cells by Cluster Analysis and Artificial Neural Networks

2003 ◽  
Vol 57 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Lin Zhang ◽  
Gary W. Small ◽  
Abigail S. Haka ◽  
Linda H. Kidder ◽  
E. Neil Lewis
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Fourier Transform ◽  
Artificial Neural Networks ◽  
Fourier Transform Infrared ◽  
Carcinoma Cells ◽  
Human Breast ◽  
Microscopic Imaging ◽  
Artificial Neural

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden–Fletcher–Goldfarb–Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

On-line determination of ore type using cluster analysis and neural networks

1995 ◽  
Vol 8 (6) ◽  
pp. 637-648 ◽  
Author(s):  
S Laine ◽  
H Lappalainen ◽  
S.-L Jämsä-Jounela
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
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