scholarly journals From Paths to Routes: A Method for Path Classification

2021 ◽  
Vol 14 ◽  
Author(s):  
Andrea Gonsek ◽  
Manon Jeschke ◽  
Silvia Rönnau ◽  
Olivier J. N. Bertrand
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Distance Functions ◽  
Consensus Clustering ◽  
Time Warping ◽  
Cluttered Environment ◽  
Qualitative Classification ◽  
One Step ◽  
Novel Method ◽  
Dynamic Time

Many animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance traveled, or the amount of meandering) or were visually classified into categories by the experimenters. However, similar quantities obtained from such descriptors do not guarantee similar paths, and qualitative classification by experimenters is prone to observer biases. Here we propose a novel method to classify paths based on their similarity with different distance functions and clustering algorithms based on the trajectories of bumblebees flying through a cluttered environment. We established a method based on two distance functions (Dynamic Time Warping and Fréchet Distance). For all combinations of trajectories, the distance was calculated with each measure. Based on these distance values, we grouped similar trajectories by applying the Monte Carlo Reference-Based Consensus Clustering algorithm. Our procedure provides new options for trajectory analysis based on path similarities in a variety of experimental paradigms.

scholarly journals Using compression to find interesting one-dimensional cellular automata

2019 ◽  
Vol 6 (1) ◽  
pp. 123-146
Author(s):  
Nadim Ahmed ◽  
William J. Teahan
Keyword(s):  
Cellular Automata ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Rate Of Change ◽  
Cross Entropy ◽  
Compression Scheme ◽  
Partial Matching ◽  
One Dimensional ◽  
Sierpinski Triangle ◽  
Novel Method

Abstract This paper proposes a novel method for finding interesting behaviour in complex systems based on compression. A new clustering algorithm has been designed and applied specifically for clustering 1D elementary cellular automata behaviour using the prediction by partial matching (PPM) compression scheme, with the results gathered to find interesting behaviours. This new algorithm is then compared with other clustering algorithms in Weka and the new algorithm is found to be more effective at grouping behaviour that is visually similar in output. Using PPM compression, the rate of change of the cross-entropy with respect to time is calculated. These values are used in combination with a clustering algorithm, such as k-means, to create a new set of clusters for cellular automata. An analysis of the data in each cluster is then used to determine if a cluster can be classed as interesting. The clustering algorithm itself was able to find unusual behaviours, such as rules 167 and 181 which have output that is slightly different from all the other Sierpiński Triangle-like patterns, because their apexes are off-centre by one cell. When comparing the new algorithm with other established ones, it was discovered that the new algorithm was more effective in its ability to group interesting and unusual cellular automata behaviours together.

scholarly journals An Ensemble of Locally Reliable Cluster Solutions

2020 ◽  
Vol 10 (5) ◽  
pp. 1891 ◽  
Author(s):  
Huan Niu ◽  
Nasim Khozouie ◽  
Hamid Parvin ◽  
Hamid Alinejad-Rokny ◽  
Amin Beheshti ◽  
...  
Keyword(s):  
Simple Model ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Consensus Clustering ◽  
Weak Base ◽  
Clustering Ensemble ◽  
High Quality ◽  
Reference Set ◽  
Cluster Solutions ◽  
Better Than

Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.

scholarly journals Energy profiling of end-users in service and industry sectors with use of Complex Network Analysis

2021 ◽  
Vol 312 ◽  
pp. 10001
Author(s):  
Rosario Portera ◽  
Fabrizio Bonacina ◽  
Alessandro Corsini ◽  
Eric Stefan Miele ◽  
Lorenzo Ricciardi Celsi
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Clustering Algorithm ◽  
Temporal Dynamics ◽  
Clustering Algorithms ◽  
Energy Systems ◽  
End Users ◽  
Complex Network Analysis ◽  
Industrial Sectors ◽  
Dynamic Time

Decarbonization scenarios advocate the transformation of energy systems to a decentralized grid of prosumers. However, in heterogeneous energy systems, profiling of end-users is still to be investigated. As a matter of fact, the knowledge of electrical load dynamics is instrumental to the system efficiency and the optimization of energy dispatch strategies. Recently, a number of clustering algorithms have been proposed to group load diagrams with similar shapes, generating typical profiles. To this end, conventional clustering algorithms are unable to capture the temporal dynamics and sequential relationships among data. This circumstance is of paramount importance in the service and industrial sectors where energy consumption trends over time are possibly non-stationary. In this paper, we aim to reconstruct the annual user energy profile identified through a non-conventional method which combines a time series clustering algorithm, namely K-Means with Dynamic Time Warping, with Complex Network Analysis. For the purpose of the present research, we have used an open database containing the data of 100 commercial and industrial consumers, collected every 5 minutes over a year. From the results, it is possible to identify different patterns of consumer behaviour and similar corporate profiles without any prior knowledge of the raw data.

scholarly journals A Robot Learning Method with Physiological Interface for Teleoperation Systems

2019 ◽  
Vol 9 (10) ◽  
pp. 2099 ◽  
Author(s):  
Jing Luo ◽  
Chenguang Yang ◽  
Hang Su ◽  
Chao Liu
Keyword(s):  
Human Robot Interaction ◽  
Human Operator ◽  
Specific Information ◽  
Time Warping ◽  
Robot Interaction ◽  
Working Environments ◽  
Teleoperation System ◽  
Novel Method ◽  
Dynamic Time ◽  
Teleoperation Systems

The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environments, the manipulator’s performance and efficiency of the human-robot interaction in the tasks may degrade significantly. In this study, a novel method of human-centric interaction, through a physiological interface was presented to capture the information details of the remote operation environments. Simultaneously, in order to relieve workload of the human operator and to improve efficiency of the teleoperation system, an updated regression method was proposed to build up a nonlinear model of demonstration for the prescribed task. Considering that the demonstration data were of various lengths, dynamic time warping algorithm was employed first to synchronize the data over time before proceeding with other steps. The novelty of this method lies in the fact that both the task-specific information and the muscle parameters from the human operator have been taken into account in a single task; therefore, a more natural and safer interaction between the human and the robot could be achieved. The feasibility of the proposed method was demonstrated by experimental results.

MODIS NDVI time series clustering under dynamic time warping

2014 ◽  
Vol 12 (05) ◽  
pp. 1461011 ◽  
Author(s):  
Zheng Zhang ◽  
Ping Tang ◽  
Lianzhi Huo ◽  
Zengguang Zhou
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Clustering Algorithm ◽  
Experimental Result ◽  
Time Warping ◽  
Time Series Clustering ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Time Distortion ◽  
Dynamic Time

For MODIS NDVI time series with cloud noise and time distortion, we propose an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework is dynamic time warping (DTW) distance and its corresponding averaging method, DTW barycenter averaging (DBA). We used 12 years of MODIS NDVI time series to perform annual land-cover clustering in Poyang Lake Wetlands. The experimental result shows that our method performs better than classic clustering based on ordinary Euclidean methods.

Least-squares horizons with local slopes and multigrid correlations

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. IM29-IM40 ◽  
Author(s):  
Xinming Wu ◽  
Sergey Fomel
Keyword(s):  
Least Squares ◽  
Extraction Methods ◽  
Structural Features ◽  
Coarse Grid ◽  
Time Warping ◽  
Fine Grid ◽  
Seismic Amplitude ◽  
Novel Method ◽  
Dynamic Time ◽  
Fitting In

Most seismic horizon extraction methods are based on seismic local reflection slopes that locally follow seismic structural features. However, these methods often fail to correctly track horizons across discontinuities such as faults and noise because the local slopes can only correctly follow laterally continuous reflections. In addition, seismic amplitude or phase information is not used in these methods to compute horizons that follow a consistent phase (e.g., peaks or troughs). To solve these problems, we have developed a novel method to compute horizons that globally fit the local slopes and multigrid correlations of seismic traces. In this method, we first estimate local reflection slopes by using structure tensors and compute laterally multigrid slopes by using dynamic time warping (DTW) to correlate seismic traces within multiple laterally coarse grids. These coarse-grid slopes can correctly correlate reflections that may be significantly dislocated by faults or other discontinuous structures. Then, we compute a horizon by fitting, in the least-squares sense, the slopes of the horizon with the local reflection slopes and multigrid slopes or correlations computed by DTW. In this least-squares system, the local slopes on the fine grid and the multiple coarse-grid slopes will fit a consistent horizon in areas without lateral discontinuities. Across laterally discontinuous areas where the local slopes fail to correctly correlate reflections and mislead the horizon extraction, the coarse-grid slopes will help to find the corresponding reflections and correct the horizon extraction. In addition, the multigrid correlations or slopes computed by dynamic warping can also assist in computing phase-consistent horizons. We apply the proposed horizon extraction method to multiple 2D and 3D examples and obtain accurate horizons that follow consistent phases and correctly track reflections across faults.

scholarly journals M3C: Monte Carlo reference-based consensus clustering

2018 ◽  
Author(s):  
Christopher R. John ◽  
David Watson ◽  
Dominic Russ ◽  
Katriona Goldmann ◽  
Michael Ehrenstein ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Data ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Null Distributions ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Cancer Genome Atlas

AbstractGenome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters.

scholarly journals Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing

2016 ◽  
Vol 27 (02) ◽  
pp. 1650042 ◽  
Author(s):  
Chao Liu ◽  
Basel Abu-Jamous ◽  
Elvira Brattico ◽  
Asoke K. Nandi
Keyword(s):  
Auditory Processing ◽  
Clustering Algorithm ◽  
Brain Connectivity ◽  
Clustering Algorithms ◽  
Brain Structures ◽  
R Package ◽  
Affective Processing ◽  
Consensus Clustering ◽  
Clustering Methods ◽  
Analysis Methods

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package “UNCLES” available on http://cran.r-project.org/web/packages/UNCLES/index.html .

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