A clustering approach to Generalized Tree Alignment with application to Alu repeats

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.

Download Full-text

4D+SNN: A Spatio-Temporal Density-Based Clustering Approach with 4D Similarity

2013 IEEE 13th International Conference on Data Mining Workshops ◽

10.1109/icdmw.2013.119 ◽

2013 ◽

Cited By ~ 9

Author(s):

Ricardo Oliveira ◽

Maribel Yasmina Santos ◽

Joao Moura Pires

Keyword(s):

Density Based Clustering ◽

Clustering Approach ◽

Spatio Temporal

Download Full-text

Subject-Dependent Physical Activity Recognition Model Framework with a Semi-supervised Clustering Approach

2013 European Modelling Symposium ◽

10.1109/ems.2013.7 ◽

2013 ◽

Cited By ~ 2

Author(s):

Hashim Ali ◽

Enza Messina ◽

Roberto Bisiani

Keyword(s):

Physical Activity ◽

Activity Recognition ◽

Model Framework ◽

Recognition Model ◽

Supervised Clustering ◽

Clustering Approach ◽

Physical Activity Recognition

Download Full-text

Predicting Erroneous Financial Statements Using a Density-Based Clustering Approach

2020 The 4th International Conference on Business and Information Management ◽

10.1145/3418653.3418673 ◽

2020 ◽

Author(s):

Martha Tatusch ◽

Gerhard Klassen ◽

Marcus Bravidor ◽

Stefan Conrad

Keyword(s):

Financial Statements ◽

Density Based Clustering ◽

Clustering Approach

Download Full-text

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

Remote Sensing ◽

10.3390/rs13112125 ◽

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).

Download Full-text