scholarly journals Identifying and Analyzing the Prevalent Regions of a Co-Location Pattern Using Polygons Clustering Approach

2017 ◽  
Vol 6 (9) ◽  
pp. 259 ◽  
Author(s):  
Wenhao Yu
Keyword(s):  
Location Pattern ◽  
Clustering Approach

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

scholarly journals Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2125
Author(s):  
Bardia Yousefi ◽  
Clemente Ibarra-Castanedo ◽  
Martin Chamberland ◽  
Xavier P. V. Maldague ◽  
Georges Beaudoin
Keyword(s):  
Matched Filter ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Clustering Methods ◽  
Spectral Angle Mapper ◽  
Long Wave ◽  
Clustering Approach ◽  
Spectral Comparison ◽  
Computational Simplicity ◽  
Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

scholarly journals The Impact of the Ring Roads on the Location Pattern of Shops in Town and City Centres. A Space Syntax Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 3927
Author(s):  
Akkelies van Nes
Keyword(s):  
Urban Areas ◽  
Space Syntax ◽  
Optimal Location ◽  
Spatial Integration ◽  
Street Network ◽  
Location Pattern ◽  
Ring Road ◽  
Road Projects ◽  
The Impact ◽  
City Centres

This contribution demonstrates how inner ring roads change the location pattern of shops in urban areas with the application of the space syntax method. A market rational behaviour persists, in that shop owners always search for an optimal location to reach as many customers as possible. If the accessibility to this optimal location is affected by changes in a city’s road and street structure, it will affect the location pattern of shops. Initially, case studies of inner ring road projects in Birmingham, Coventry, Wolverhampton, Bristol, Tampere, and Mannheim show how their realisation affect the spatial structure of the street network of these cities and the location pattern of shops. The results of the spatial integration analyses of the street and road network are discussed with reference to changes in land-use before and after the implementation of ring roads, and current space syntax theories. As the results show, how an inner ring road is connected to and the type of the street network it is imposed upon dictates the resulting location pattern of shops. Shops locate and relocate themselves along the most spatially-integrated streets. Evidence on how new road projects influence the location pattern of shops in urban centres are useful for planning sustainable city centres.

An Overlapping Clustering Approach for Precision, Diversity and Novelty-Aware Recommendations

2021 ◽  
pp. 114917
Author(s):  
Chems Eddine Berbague ◽  
Nour El-islam Karabadji ◽  
Hassina Seridi ◽  
Panagiotis Symeonidis ◽  
Yannis Manolopoulos ◽  
...  
Keyword(s):  
Overlapping Clustering ◽  
Clustering Approach
Sign in / Sign up

Export Citation Format

Share Document