1. The document describes a heuristic approach for solving the cluster traveling salesman problem (CTSP) using genetic algorithms.
2. The proposed algorithm divides nodes into pre-specified clusters, uses GA to find a Hamiltonian path for each cluster, then combines the optimized cluster paths to form a full tour.
3. The algorithm was tested on symmetric TSPLIB instances and shown to find high quality solutions faster than two other metaheuristic approaches for CTSP.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Quantum inspired evolutionary algorithm for solving multiple travelling sales...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A Counterexample to the Forward Recursion in Fuzzy Critical Path Analysis Und...ijfls
This document presents a counterexample demonstrating that the fuzzy forward recursion method for determining critical paths does not always produce results consistent with the extension principle when discrete fuzzy sets are used to represent activity durations.
The document first provides background on fuzzy sets and critical path analysis. It then presents a proposition stating that the membership function for fuzzy critical path lengths can be determined by taking the maximum of the minimum membership values across all activity durations in each configuration.
The document goes on to present a counterexample using a simple series-parallel network with 18 configurations. It shows that applying the fuzzy forward recursion produces a different membership value for one critical path length compared to directly applying the extension principle. This difference proves the fuzzy forward
This document discusses a hybridization of the Magnetic Charge System Search (MCSS) method for efficient data clustering. MCSS is a meta-heuristic algorithm inspired by electromagnetic theory that has shown potential but also has issues with convergence rate and getting stuck in local optima. The authors propose a Hybrid MCSS (HMCSS) that incorporates a local search strategy and differential evolution inspired updating to improve convergence. An experiment on benchmark functions and real clustering problems shows HMCSS provides better results than existing algorithms and enhances MCSS convergence.
HEATED WIND PARTICLE’S BEHAVIOURAL STUDY BY THE CONTINUOUS WAVELET TRANSFORM ...cscpconf
Nowadays Continuous Wavelet Transform (CWT) as well as Fractal analysis is generally used for the Signal and Image processing application purpose. Our current work extends the field of application in case of CWT as well as Fractal analysis by applying it in case of the agitated wind particle’s behavioral study. In this current work in case of the agitated wind particle, we have mathematically showed that the wind particle’s movement exhibits the “Uncorrelated” characteristics during the convectional flow of it. It is also demonstrated here by the Continuous Wavelet Transform (CWT) as well as the Fractal analysis with matlab 7.12 version
This document introduces an R package called PSF that implements a Pattern Sequence based Forecasting (PSF) algorithm for univariate time series forecasting. The PSF algorithm clusters time series data and then predicts future values based on identifying repeating patterns of clusters. The PSF package contains functions that perform the main steps of the PSF algorithm, including selecting the optimal number of clusters, selecting the optimal window size, and making predictions for a given window size and number of clusters. The package aims to promote and simplify the use of the PSF algorithm for time series forecasting.
A Non Parametric Estimation Based Underwater Target ClassifierCSCJournals
Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Quantum inspired evolutionary algorithm for solving multiple travelling sales...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A Counterexample to the Forward Recursion in Fuzzy Critical Path Analysis Und...ijfls
This document presents a counterexample demonstrating that the fuzzy forward recursion method for determining critical paths does not always produce results consistent with the extension principle when discrete fuzzy sets are used to represent activity durations.
The document first provides background on fuzzy sets and critical path analysis. It then presents a proposition stating that the membership function for fuzzy critical path lengths can be determined by taking the maximum of the minimum membership values across all activity durations in each configuration.
The document goes on to present a counterexample using a simple series-parallel network with 18 configurations. It shows that applying the fuzzy forward recursion produces a different membership value for one critical path length compared to directly applying the extension principle. This difference proves the fuzzy forward
This document discusses a hybridization of the Magnetic Charge System Search (MCSS) method for efficient data clustering. MCSS is a meta-heuristic algorithm inspired by electromagnetic theory that has shown potential but also has issues with convergence rate and getting stuck in local optima. The authors propose a Hybrid MCSS (HMCSS) that incorporates a local search strategy and differential evolution inspired updating to improve convergence. An experiment on benchmark functions and real clustering problems shows HMCSS provides better results than existing algorithms and enhances MCSS convergence.
HEATED WIND PARTICLE’S BEHAVIOURAL STUDY BY THE CONTINUOUS WAVELET TRANSFORM ...cscpconf
Nowadays Continuous Wavelet Transform (CWT) as well as Fractal analysis is generally used for the Signal and Image processing application purpose. Our current work extends the field of application in case of CWT as well as Fractal analysis by applying it in case of the agitated wind particle’s behavioral study. In this current work in case of the agitated wind particle, we have mathematically showed that the wind particle’s movement exhibits the “Uncorrelated” characteristics during the convectional flow of it. It is also demonstrated here by the Continuous Wavelet Transform (CWT) as well as the Fractal analysis with matlab 7.12 version
This document introduces an R package called PSF that implements a Pattern Sequence based Forecasting (PSF) algorithm for univariate time series forecasting. The PSF algorithm clusters time series data and then predicts future values based on identifying repeating patterns of clusters. The PSF package contains functions that perform the main steps of the PSF algorithm, including selecting the optimal number of clusters, selecting the optimal window size, and making predictions for a given window size and number of clusters. The package aims to promote and simplify the use of the PSF algorithm for time series forecasting.
A Non Parametric Estimation Based Underwater Target ClassifierCSCJournals
Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
This document proposes and evaluates a new metaheuristic optimization algorithm called Current Search (CS) and applies it to optimize PID controller parameters for DC motor speed control. The CS is inspired by electric current flow and aims to balance exploration and exploitation. It outperforms genetic algorithm, particle swarm optimization, and adaptive tabu search on benchmark optimization problems, finding better solutions faster. When applied to optimize a PID controller for DC motor speed control, the CS successfully controlled motor speed.
Soft Computing Techniques Based Image Classification using Support Vector Mac...ijtsrd
n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set. Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla ""Soft Computing Techniques Based Image Classification using Support Vector Machine Performance"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal
α Nearness ant colony system with adaptive strategies for the traveling sales...ijfcstjournal
On account of ant colony algorithm easy to fall into local optimum, this paper presents an improved ant
colony optimization called α-AACS and reports its performance. At first, we provide an concise description
of the original ant colony system(ACS) and introduce α-nearness based on the minimum 1-tree for ACS’s
disadvantage, which better reflects the chances of a given link being a member of an optimal tour. Then, we
improve α-nearness by computing a lower bound and propose other adaptations for ACS. Finally, we
conduct a fair competition between our algorithm and others. The results clearly show that α-AACS has a
better global searching ability in finding the best solutions, which indicates that α-AACS is an effective
approach for solving the traveling salesman problem.
2018 algorithms for the minmax regret path problem with interval dataFrancisco Pérez
This document summarizes a research paper that studies the Minmax Regret Path Problem with interval data. The paper presents a new exact branch and cut algorithm for solving this problem and also proposes new heuristics, including a local search heuristic and a simulated annealing metaheuristic that uses a novel neighborhood structure. Computational experiments on benchmark instances are conducted to analyze the performance of the different algorithms and approaches. The results provide an assessment of the algorithms and show the superiority of the simulated annealing approach for finding good solutions to large problem instances.
The document discusses applying random distortion testing (RDT) in a spectral clustering context. RDT is a framework for guaranteeing a false alarm probability threshold in detecting distorted data using threshold-based tests. The document introduces RDT and spectral clustering concepts. It then proposes using the p-value from RDT as the similarity function or kernel in spectral clustering, to handle disturbed data. Experiments are conducted to compare the partitioning performance of the RDT p-value kernel to the Gaussian kernel.
This summarizes a document about a filter-and-refine approach for reducing computational cost when performing correlation analysis on pairs of spatial time series datasets. It groups similar time series within each dataset into "cones" based on spatial autocorrelation. Cone-level correlation computation can then filter out many element pairs whose correlation is clearly below a threshold. The remaining pairs require individual correlation computation in the refinement phase. Experiments on Earth science datasets showed significant computational savings, especially with high correlation thresholds.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
This document presents a new algorithm for flexible route planning that allows customizing a linear combination of two metrics like travel time and cost. The algorithm precomputes shortcuts for a graph based on contracting nodes in a customized order. It develops the concept of "gradual parameter interval splitting" to improve the node ordering for different parameter values. The algorithm combines node contraction with a goal-directed technique to further improve performance of flexible queries.
The document discusses an algorithm for discovering patterns (motifs) in time series data. It proposes a modification to an existing motif discovery algorithm to improve performance by eliminating the influence of adjacent subsequences. The modified algorithm is compared to the original algorithm based on metrics like number of motifs discovered, execution time, and mean distance of motifs from the main pattern (1-motif), with the modification showing same or better performance in all cases tested.
Novel analysis of transition probabilities in randomized k sat algorithmijfcstjournal
This document summarizes a research paper that proposes a new analysis of transition probabilities in randomized k-SAT algorithms. Specifically:
- It shows the probability of correctly flipping a literal in 2-SAT and 3-SAT approaches 2/3 and 4/7 respectively, using Karnaugh maps to analyze all possible variable combinations.
- It extends this analysis to general k-SAT, showing the transition probability of the Markov chain in randomized k-SAT algorithms approaches 0.5.
- Using this result, it determines the probability and complexity of finding a satisfying assignment for randomized k-SAT, showing values within a polynomial factor of (0.9272)^n and (1.0785)^n for satisf
This document summarizes a research paper that proposes a new method to accelerate the nearest neighbor search step of the k-means clustering algorithm. The k-means algorithm is computationally expensive due to calculating distances between data points and cluster centers. The proposed method uses geometric relationships between data points and centers to reject centers that are unlikely to be the nearest neighbor, without decreasing clustering accuracy. Experimental results showed the method significantly reduced the number of distance computations required.
THE RESEARCH OF QUANTUM PHASE ESTIMATION ALGORITHMIJCSEA Journal
This document discusses phase estimation in quantum computing. It begins by introducing quantum Fourier transforms and how they are important for algorithms like Shor's algorithm. It then describes the phase estimation algorithm in detail, including how it uses two registers to estimate the phase of a quantum state and how the inverse quantum Fourier transform improves this estimate. Simulation results are presented that show the probability distribution of the estimated phase converging to the true value and how the probability of success increases with more qubits while computational costs rise polynomially. The paper concludes that the optimal number of qubits balances high success probability and low costs for phase estimation.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
Quantum algorithm for solving linear systems of equationsXequeMateShannon
Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b, find a vector x such that Ax=b. We consider the case where one doesn't need to know the solution x itself, but rather an approximation of the expectation value of some operator associated with x, e.g., x'Mx for some matrix M. In this case, when A is sparse, N by N and has condition number kappa, classical algorithms can find x and estimate x'Mx in O(N sqrt(kappa)) time. Here, we exhibit a quantum algorithm for this task that runs in poly(log N, kappa) time, an exponential improvement over the best classical algorithm.
Resource theory of asymmetric distinguishabilityMark Wilde
We systematically develop the resource-theoretic perspective on distinguishability. The theory is a resource theory of asymmetric distinguishability, given that approximation is allowed for the first quantum state in general transformation tasks. We introduce bits of asymmetric distinguishability as the basic currency in this resource theory, and we prove that it is a reversible resource theory in the asymptotic limit, with the quantum relative entropy being the fundamental rate of resource interconversion. We formally define the distillation and dilution tasks, and we find that the exact one-shot distillable distinguishability is equal to the min-relative entropy, the exact one-shot distinguishability cost is equal to the max-relative entropy, the approximate one-shot distillable distinguishability is equal to the smooth min-relative entropy, and the approximate one-shot distinguishability cost is equal to the smooth max-relative entropy. We also develop the resource theory of asymmetric distinguishability for quantum channels. For this setting, we prove that the exact distinguishability cost is equal to channel max-relative entropy and the distillable distinguishability is equal to the amortized channel relative entropy.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
On selection of periodic kernels parameters in time series predictioncsandit
This document discusses parameter selection for periodic kernels used in time series prediction. Periodic kernels are a type of kernel function used in kernel regression to perform nonparametric time series prediction. The document examines how the parameters of two periodic kernels - the first periodic kernel (FPK) and second periodic kernel (SPK) - influence prediction error. It presents an easy methodology for finding parameter values based on grid search. This methodology was tested on benchmark and real datasets and showed satisfactory results.
The asynchronous parallel algorithms are developed to solve massive optimization problems in a distributed data system, which can be run in parallel on multiple nodes with little or no synchronization. Recently they have been successfully implemented to solve a range of difficult problems in practice. However, the existing theories are mostly based on fairly restrictive assumptions on the delays, and cannot explain the convergence and speedup properties of such algorithms. In this talk we will give an overview on distributed optimization, and discuss some new theoretical results on the convergence of asynchronous parallel stochastic gradient algorithm with unbounded delays. Simulated and real data will be used to demonstrate the practical implication of these theoretical results.
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
The document presents a mathematical programming approach for selecting important variables in cluster analysis. It formulates a nonlinear binary model to minimize the distance between observations within clusters, using indicator variables to select important variables. The model is applied to a sample dataset of 30 observations across 5 variables, correctly identifying variables 3, 4 and 5 as most important for clustering the observations into two groups. The results are compared to an existing variable selection heuristic, with the mathematical programming approach achieving a 100% correct classification versus 97% for the other method.
An Adaptive Masker for the Differential Evolution AlgorithmIOSR Journals
The document proposes an adaptive masker technique for the differential evolution algorithm to perform automatic fuzzy clustering. The adaptive masker aims to guide the search process towards the optimal clustering solution by dividing the mask matrix into three zones - a best masks zone, a global best influence zone where the number of clusters is a function of the best fitness, and a random zone. Experimental results on a remote sensing dataset show the proposed adaptive masker differential evolution algorithm performs better than other fuzzy clustering algorithms like iterative fuzzy c-means, improved differential evolution, and variable length genetic algorithm based fuzzy clustering in automatically detecting the optimal number of clusters.
A quantum-inspired optimization heuristic for the multiple sequence alignment...Konstantinos Giannakis
The document presents a quantum-inspired heuristic for solving the multiple sequence alignment problem in bioinformatics. It models the sequence similarity as a traveling salesman problem instance using a normalized similarity matrix. The method applies a quantum-inspired generalized variable neighborhood search metaheuristic to approximate the shortest Hamiltonian path and generate an initial alignment. Evaluation on real biological sequences shows it outperforms progressive methods, producing alignments with good sum-of-pairs scores, especially for large sequence sets.
This document proposes and evaluates a new metaheuristic optimization algorithm called Current Search (CS) and applies it to optimize PID controller parameters for DC motor speed control. The CS is inspired by electric current flow and aims to balance exploration and exploitation. It outperforms genetic algorithm, particle swarm optimization, and adaptive tabu search on benchmark optimization problems, finding better solutions faster. When applied to optimize a PID controller for DC motor speed control, the CS successfully controlled motor speed.
Soft Computing Techniques Based Image Classification using Support Vector Mac...ijtsrd
n this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine SVM on many concrete classification problems, the algorithm is not directly applicable to multi dimensional routes having different measurements. Training support vector machines SVM with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi definite PSD and Non PSD, with a statistically significant evidence while also retaining sparsity of the support vector set. Tarun Jaiswal | Dr. S. Jaiswal | Dr. Ragini Shukla ""Soft Computing Techniques Based Image Classification using Support Vector Machine Performance"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23437.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23437/soft-computing-techniques-based-image-classification-using-support-vector-machine-performance/tarun-jaiswal
α Nearness ant colony system with adaptive strategies for the traveling sales...ijfcstjournal
On account of ant colony algorithm easy to fall into local optimum, this paper presents an improved ant
colony optimization called α-AACS and reports its performance. At first, we provide an concise description
of the original ant colony system(ACS) and introduce α-nearness based on the minimum 1-tree for ACS’s
disadvantage, which better reflects the chances of a given link being a member of an optimal tour. Then, we
improve α-nearness by computing a lower bound and propose other adaptations for ACS. Finally, we
conduct a fair competition between our algorithm and others. The results clearly show that α-AACS has a
better global searching ability in finding the best solutions, which indicates that α-AACS is an effective
approach for solving the traveling salesman problem.
2018 algorithms for the minmax regret path problem with interval dataFrancisco Pérez
This document summarizes a research paper that studies the Minmax Regret Path Problem with interval data. The paper presents a new exact branch and cut algorithm for solving this problem and also proposes new heuristics, including a local search heuristic and a simulated annealing metaheuristic that uses a novel neighborhood structure. Computational experiments on benchmark instances are conducted to analyze the performance of the different algorithms and approaches. The results provide an assessment of the algorithms and show the superiority of the simulated annealing approach for finding good solutions to large problem instances.
The document discusses applying random distortion testing (RDT) in a spectral clustering context. RDT is a framework for guaranteeing a false alarm probability threshold in detecting distorted data using threshold-based tests. The document introduces RDT and spectral clustering concepts. It then proposes using the p-value from RDT as the similarity function or kernel in spectral clustering, to handle disturbed data. Experiments are conducted to compare the partitioning performance of the RDT p-value kernel to the Gaussian kernel.
This summarizes a document about a filter-and-refine approach for reducing computational cost when performing correlation analysis on pairs of spatial time series datasets. It groups similar time series within each dataset into "cones" based on spatial autocorrelation. Cone-level correlation computation can then filter out many element pairs whose correlation is clearly below a threshold. The remaining pairs require individual correlation computation in the refinement phase. Experiments on Earth science datasets showed significant computational savings, especially with high correlation thresholds.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
This document presents a new algorithm for flexible route planning that allows customizing a linear combination of two metrics like travel time and cost. The algorithm precomputes shortcuts for a graph based on contracting nodes in a customized order. It develops the concept of "gradual parameter interval splitting" to improve the node ordering for different parameter values. The algorithm combines node contraction with a goal-directed technique to further improve performance of flexible queries.
The document discusses an algorithm for discovering patterns (motifs) in time series data. It proposes a modification to an existing motif discovery algorithm to improve performance by eliminating the influence of adjacent subsequences. The modified algorithm is compared to the original algorithm based on metrics like number of motifs discovered, execution time, and mean distance of motifs from the main pattern (1-motif), with the modification showing same or better performance in all cases tested.
Novel analysis of transition probabilities in randomized k sat algorithmijfcstjournal
This document summarizes a research paper that proposes a new analysis of transition probabilities in randomized k-SAT algorithms. Specifically:
- It shows the probability of correctly flipping a literal in 2-SAT and 3-SAT approaches 2/3 and 4/7 respectively, using Karnaugh maps to analyze all possible variable combinations.
- It extends this analysis to general k-SAT, showing the transition probability of the Markov chain in randomized k-SAT algorithms approaches 0.5.
- Using this result, it determines the probability and complexity of finding a satisfying assignment for randomized k-SAT, showing values within a polynomial factor of (0.9272)^n and (1.0785)^n for satisf
This document summarizes a research paper that proposes a new method to accelerate the nearest neighbor search step of the k-means clustering algorithm. The k-means algorithm is computationally expensive due to calculating distances between data points and cluster centers. The proposed method uses geometric relationships between data points and centers to reject centers that are unlikely to be the nearest neighbor, without decreasing clustering accuracy. Experimental results showed the method significantly reduced the number of distance computations required.
THE RESEARCH OF QUANTUM PHASE ESTIMATION ALGORITHMIJCSEA Journal
This document discusses phase estimation in quantum computing. It begins by introducing quantum Fourier transforms and how they are important for algorithms like Shor's algorithm. It then describes the phase estimation algorithm in detail, including how it uses two registers to estimate the phase of a quantum state and how the inverse quantum Fourier transform improves this estimate. Simulation results are presented that show the probability distribution of the estimated phase converging to the true value and how the probability of success increases with more qubits while computational costs rise polynomially. The paper concludes that the optimal number of qubits balances high success probability and low costs for phase estimation.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
Quantum algorithm for solving linear systems of equationsXequeMateShannon
Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b, find a vector x such that Ax=b. We consider the case where one doesn't need to know the solution x itself, but rather an approximation of the expectation value of some operator associated with x, e.g., x'Mx for some matrix M. In this case, when A is sparse, N by N and has condition number kappa, classical algorithms can find x and estimate x'Mx in O(N sqrt(kappa)) time. Here, we exhibit a quantum algorithm for this task that runs in poly(log N, kappa) time, an exponential improvement over the best classical algorithm.
Resource theory of asymmetric distinguishabilityMark Wilde
We systematically develop the resource-theoretic perspective on distinguishability. The theory is a resource theory of asymmetric distinguishability, given that approximation is allowed for the first quantum state in general transformation tasks. We introduce bits of asymmetric distinguishability as the basic currency in this resource theory, and we prove that it is a reversible resource theory in the asymptotic limit, with the quantum relative entropy being the fundamental rate of resource interconversion. We formally define the distillation and dilution tasks, and we find that the exact one-shot distillable distinguishability is equal to the min-relative entropy, the exact one-shot distinguishability cost is equal to the max-relative entropy, the approximate one-shot distillable distinguishability is equal to the smooth min-relative entropy, and the approximate one-shot distinguishability cost is equal to the smooth max-relative entropy. We also develop the resource theory of asymmetric distinguishability for quantum channels. For this setting, we prove that the exact distinguishability cost is equal to channel max-relative entropy and the distillable distinguishability is equal to the amortized channel relative entropy.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
On selection of periodic kernels parameters in time series predictioncsandit
This document discusses parameter selection for periodic kernels used in time series prediction. Periodic kernels are a type of kernel function used in kernel regression to perform nonparametric time series prediction. The document examines how the parameters of two periodic kernels - the first periodic kernel (FPK) and second periodic kernel (SPK) - influence prediction error. It presents an easy methodology for finding parameter values based on grid search. This methodology was tested on benchmark and real datasets and showed satisfactory results.
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1. See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/339025087
A Heuristic Approach for Cluster TSP
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DOI: 10.1007/978-3-030-34152-7_4
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Book Title Recent Advances in Intelligent Information Systems and Applied Mathematics
Series Title
Chapter Title A Heuristic Approach for Cluster TSP
Copyright Year 2020
Copyright HolderName Springer Nature Switzerland AG
Author Family Name Manna
Particle
Given Name Apurba
Prefix
Suffix
Role
Division Department of Computer Science
Organization P. K. College
Address Contai, Purba Medinipur, 721404, W.B., India
Email apurba.manna2008@gmail.com
Author Family Name Maity
Particle
Given Name Samir
Prefix
Suffix
Role
Division
Organization OM Group, Indian Institute of Management
Address Calcutta, India
Email samirm@iimcal.ac.in
Corresponding Author Family Name Roy
Particle
Given Name Arindam
Prefix
Suffix
Role
Division Department of Computer Science
Organization P. K. College
Address Contai, Purba Medinipur, 721404, W.B., India
Email royarindamroy@yahoo.com
Abstract This investigation took an attempt to solve the cluster traveling salesman problem (CTSP) by the heuristic
approach. In this problem, nodes are clustered with given a set of vertices (nodes). Given the set of vertices
is divided into a prespecified number of clusters. The size of each cluster is also pre-specified. The main
aim is to find the least cost Hamiltonian tour based on the given vertices. Here vertices of each cluster
visited contiguously, and the clusters are visited in a specific order. Standard GA is used to find a
Hamiltonian path for each cluster. The performance of the algorithm has been examined against two
3. existing algorithms for some symmetric TSPLIB instances of various sizes. The computational results
show the proposed algorithm works well among the studied metaheuristics regarding the best result and
computational time.
Keywords Cluster TSP - GA - Heuristic
4. A Heuristic Approach for Cluster TSP
Apurba Manna1
, Samir Maity2
, and Arindam Roy1(B)
1
Department of Computer Science, P. K. College, Contai,
Purba Medinipur 721404, W.B., India
apurba.manna2008@gmail.com, royarindamroy@yahoo.com
2
OM Group, Indian Institute of Management, Calcutta, India
samirm@iimcal.ac.in
Abstract. This investigation took an attempt to solve the cluster trav-
eling salesman problem (CTSP) by the heuristic approach. In this prob-
lem, nodes are clustered with given a set of vertices (nodes). Given the
set of vertices is divided into a prespecified number of clusters. The size
of each cluster is also pre-specified. The main aim is to find the least cost
Hamiltonian tour based on the given vertices. Here vertices of each clus-
ter visited contiguously, and the clusters are visited in a specific order.
Standard GA is used to find a Hamiltonian path for each cluster. The
performance of the algorithm has been examined against two existing
algorithms for some symmetric TSPLIB instances of various sizes. The
computational results show the proposed algorithm works well among
the studied metaheuristics regarding the best result and computational
time.
Keywords: Cluster TSP · GA · Heuristic
1 Introduction
Traveling salesman problem (TSP) has many different variations. The clustered
traveling salesman problem (CTSP) is one of them. At first, CTSP was proposed
by Chisman [4]. Different approaches are taken by various researcher during last
decades to solve cluster traveling salesman problem (CTSP). Few of them are
New Hybrid Heuristic approach by Mestria [11], using Neighborhood Random
Local Search a heuristic approach by Mestria [10], another approach is based on
with d-relaxed priority rule by Phuong et al. [12], a Metaheuristic approach by
Zhang et al. [15], applying the Lin-Kernighan-Helsgaun Algorithm by Helsgaun
[5], etc. CTSP is defined as follows: consider a complete undirected graph G.
Where, G = (V, E). Here V = set of vertices and E = set of edges. If the number
of node is N, then V = {v1, v2, v3, · · · , vN } and it is divided into K prespeci-
fied clusters. The prespecified clusters are {C1, C2, C3, · · · , Ck}. A cost matrix
COST = [cij] is present. This matrix represents the travel costs, distances, or
travel times which is defined on the edge set E = {(vi, vj) : vi, vj ∈ V, i = j}.
Till now, different variants of CTSP is available based on different conditions.
c
Springer Nature Switzerland AG 2020
O. Castillo et al. (Eds.): ICITAM 2019, SCI 863, pp. 1–10, 2020.
https://doi.org/10.1007/978-3-030-34152-7_4
Author
Proof
5. 2 A. Manna et al.
Suppose the number of clusters is two then it is treated as TSP with backhauls
(TSPB) [8]. In the case of free CTSP, the effective number of cluster is deter-
mined dynamically, not determined by prespecified order. The routing between
clusters is also an important part of this paper. In the case of free CTSP, it is
determined simultaneously. If all variations of CTSP are colligation of classical
TSP, they are all NP-hard. In real life, CTSP is important, and it also has a
huge application like vehicle routing [3], warehouse routing [7], integrated circuit
testing [6], production planning [6], etc. Chisman [4] first proposed that CTSP
can be represented as a TSP by adding or subtracting a big impulsive constant
I to or from the cost of every inter-cluster edge. So, at the end of conversion,
a specific algorithm for the TSP also apply to solve the problem precisely. The
use of the heuristic procedure is practical in CTSP when the number of nodes
is large or very large. Most common heuristic algorithms are approximate algo-
rithms, artificial neural network, tabu search, genetic algorithm (GA) and so on.
To solve TSP and its variation, Genetic Algorithm (GA) is treated as best. Now
our proposed algorithm Heuristic Approach is a variation of GA to find the opti-
mal solution of given problem. The effectiveness of our proposed algorithm has
been compared against lexisearch algorithm (LSA) [1] and hybrid GA(HGA)
[2] for few symmetric TSPLIB [13] instances. At last, we have taken a set of
solutions of large size TSPLIB [13] instances and compared with Hybrid GA
(HGA).
The proposed algorithm have following key features:
• Cluster creation
• Genetic Algorithm (GA)
• Probabilistic selection
• Cyclic crossover
• Random crossing point
• Random mutation
• Routing between clusters
• Test on TSPLIB instances
The present paper is prepared as follows: Sect. 1, a short introduction is pro-
duced. In Sect. 2, required mathematical pre-requisite. In Sect. 3, the proposed
algorithm is presented. In Sect. 4, a numerical tests are finished. Again in Sect. 5,
a brief discussion is given. Finally, in Sect. 6, a conclusion with future scope is
studied.
2 Classical Definition of CTSP
The CTSP is outlined on a loop-free undirected graph G. Where, G = (V, E).
Here V = set of vertex and E = set of edge. If the number of node is N, then,
V = {v1, v2, v3, · · · , vN } and it is divided into K cluster. Here, K is pre-specified.
The pre-specified clusters are {C1, C2, C3, · · · , Ck}. A cost matrix COST = [cij]
between ith
and jth
node is present. This matrix represents the travel costs,
which is defined on the edge set E = {(vi, vj) : vi, vj ∈ V, i = j}. There is a
Author
Proof
6. A Heuristic Approach for Cluster TSP 3
decision variable xij, xij = 1 iff a tour completed between vi to vj, otherwise,
xij = 0. The framing of CTSP can be represented as follows:
Minimize Z =
i=j
c(i, j)xij
subject to
N
i=1
xij = 1 for j = 1, 2, ..., N
N
j=1
xij = 1 for i = 1, 2, ..., N
i∈vk
j∈vk
xij = |vk|, ∀|vk| ⊂ V, |vk| ≥ 1, k = 1, 2, 3, · · · , m
where xij ∈ {0, 1}, i, j = 1, 2, · · · , N
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⎪
⎪
⎪
⎭
(1)
Then the above CTSP reduces to
determine a complete tour (x1, x2, ..., xN , x1)
to minimize Z =
N−1
i=1
c(xi, xi+1) + c(xN , x1)
where xi = xj, i, j = 1, 2..., N.
⎫
⎪
⎪
⎬
⎪
⎪
⎭
(2)
along with sub tour elimination criteria
N
i∈S
N
j∈S
xij ≤ |S| − 1, ∀S ⊂ Q (3)
3 Proposed Heuristic Based Genetic Algorithm
A well-known heuristic based GA is used for solve the CTSP. Using GA or any
other heuristic method we get a better solution for small size TSP or medium
size TSP very easily. But when the size of TSP has increased then complexity
increases in a parallel way. To overcome this problem, TSP transformed to CTSP,
which is a variation of TSP. The proposed algorithm performs in three steps.
First, all nodes are divided into the pre-specified number of cluster. The number
of nodes in each cluster may be the same or not. Second, each cluster is optimized
using GA. Third, reconstruct a Hamiltonian cycle using all optimized cluster.
All optimized cluster contains a Hamiltonian path, not cycle.
3.1 Cluster Creation
The number of clusters is pre-specified. At first, we ensure the size of each cluster.
Then, selected nodes are inserted into each specified cluster. It is clear that every
cluster must contain a unique set of nodes. That is after the optimization of each
cluster, generate a different and unique Hamiltonian path.
Author
Proof
7. 4 A. Manna et al.
Algorithm:
1. Begin.
2. State the number of cluster.
3. Generate a random number(r) between (0 to N−1).
4. Calculate the cost(c) from node r to each node.
5. Select a node in a cluster depend on minimum cost(c).
6. Ignore the node selected in step 5.
7. Calculate the cost(c) from node r to each remain node.
8. Repeat steps 5 to 7 until all nodes are distributed based on previous cluster
size of each cluster.
9. End.
3.2 Genetic Algorithm(GA)
Proposed algorithm have the concept of the generation of the cluster. Here given
nodes are divided between pre-specified clusters based on subsection 3.1. Initially,
each cluster contains a number of nodes. Based on these nodes initial population
is created randomly. Each cluster strictly follows this step, and strictly GA is
applied to each cluster to produce a Hamiltonian path. i.e GA is applied to
optimize each cluster. So our proposed GA is as follows.
Genetic Algorithm is a well-known randomized search method. There is
a natural rule that, survival of the fittest among the species based on their gene
architecture of the chromosomes. Gene structure constructed based on random
change on it and it is evolved from one iteration(generation) to next. Every
iteration with the following three operations.
(a) Selection: It is a stochastic process which simulates the quotation
survival -of-fittest. An objective function took a vital role and based on it few
chromosomes are copied from a predefined population of the chromosome. All
selected chromosomes are used for the next operation. Our proposed algorithm
uses the Boltzman’s probabilistic selection process [9].
(b) Crossover: It is known as a binary operator. It works with a pair of
parent chromosome. Parents are selected with a significant probability, and as
a result, two new offspring chromosomes are prepared. Its importance in GA
is very much. The proposed algorithm uses Cyclic Crossover [14] as a crossover
operator.
(c) Mutation: It is known as a unary operator. It is applied to every chro-
mosome with a small probability. The mutation also important part to diversify
the GA search space. The proposed algorithm uses random mutation as a muta-
tion operator.
GA starts with a randomly generated initial population and repeat the
above three operations until the stopping criterion is satisfied. Crossover creates
a new opportunity over GA generating new offspring chromosomes. An example
of a successful heuristic algorithm to solve a classical TSP and its variations is
GA. It never gives the guarantee about the optimal solution, but it can find a
near-optimal solution in a concise time.
Author
Proof
8. A Heuristic Approach for Cluster TSP 5
3.3 Inter Cluster Re-linking
We aim to find a Hamiltonian cycle. Optimized each cluster have a Hamiltonian
path. To produce a Hamiltonian cycle we have maintained the following steps.
1. Store the number of cluster
2. Store each Hamiltonian path of each cluster
3. Calculate possible combinations of given clusters
4. Arrange the cluster sequence based on combination sequence
5. Merge each combination and prepare a final path
6. Calculate the cost of each combination
7. The Least cost combination is treated as best result of our proposed algorithm
3.4 Proposed Algorithm
1. Start
2. Input the number of cluster.
3. Define the size of each cluster.
4. To determine the nodes for each cluster, do following steps:
(A) Generate a random number(r) between (0 to N-1).
(B) Calculate the cost(c) from node r to each node.
(C) Select a node in a cluster depend on minimum cost(c).
(D) Ignore the node selected in step (C).
(E) Calculate the cost(c) from node r to each remain node.
(F) Repeat steps (C) to (E) until all nodes are distributed based on previous
cluster size of each cluster.
5. After creation of each cluster with its respective nodes, a randomly generated
population is prepared on the basis of stored nodes of each cluster.
6. Proposed GA is applied to each cluster to generate a Hamiltonian path
based on the specified nodes of each cluster.
7. Prepare possible combinations of given clusters.
8. Calculate objective function value of each combination(path).
9. Find minimum cost(objective function value) among all combinations, this
will be the best solution of our proposed algorithm.
10. Stop
4 Numerical Tests
Proposed algorithm is guided by few parameters, namely, crossover probability
(pc), mutation probability (pm) and population size (pv) and also termination
condition. Proper functioning of GA depends on a proper selection of these
parameters. Table 1 shows the comparison of performance between proposed
Heuristic based GA (HbGA), LSA [1] and HGA [2] also.
Table 2 shows a comparative study between HGA and HbGA based on sym-
metric TSPLIB instances. Taken TSPLIB instances are larger than TSPLIB
instances of Table 1.
Author
Proof
11. 8 A. Manna et al.
Table 3. (continued)
Cluster pc pm popsize result cpu-timesec Error (%)
4 0.34 0.43 50 34715 16.40 −24.09
55 37499 18.20 −18.00
60 50034 19.23 9.40
65 31569 22.18 −30.97
70 33665 24.57 −26.39
75 46783 28.07 2.30
80 40573 27.23 −11.28
85 31703 31.86 −30.68
90 44526 32.87 −2.64
Table 4. Comparative result based on different sizes cluster (pc = 0.34, pm=0.43,
popsize = 50)
Instance Cluster result cpu − timesec
kroA100 2 31186 17.37
3 38372 16.80
4 34715 16.40
5 51670 20.01
6 36372 19.73
7 53503 22.98
8 45696 22.52
9 49470 22.98
10 45106 27.63
5 Discussion
This article is a special attempt to find out a way to solve a large scale TSP
in a convenient way. Here we have chosen the way as a cluster TSP (CTSP).
Our proposed HbGA algorithm is implemented by considering some parametric
values as probability of crossover (pc), probability of mutation (pm), maximum
number of chromosome as a population (pv) and maximum generation. This pro-
posed algorithm is written in C++. It is clear from Table 1 that our proposed
HbGA algorithm is much efficient than LSA and HGA both. Results shown in
Table 1 based on 10 benchmark TSP references in TSPLIB. These ten instances
are between 16 and 51 cities. It is remarkable that our proposed HbGA is much
efficient for bays29 for 29 cities problem and eil51 for 51 cities problem also. Com-
pare to both LSA and HGA using our proposed HbGA, we got better results
than existing, which are illustrated in Table 1. Table 2 is also prove the efficiency
of HbGA based on a comparative study of instances in TSPLIB between 52 and
Author
Proof
12. A Heuristic Approach for Cluster TSP 9
417 cities. So, all over performance of HbGA is better than HGA. Table 3 is
a parametric study based on standard TSPLIB instance of 100 cities. Table 3
represents better results considering four(4) clusters and all different combina-
tion of parametric values by using our proposed HbGA. Also it is remarkably
mention that, we got these better results within less CPU time than existing.
From Table 4 we can observe that cluster size two(2) gives the better results
than cluster size four(4). From above discussion, we may come to an end that
our proposed HbGA is also applicable for solving real life optimization problems.
6 Conclusion
The present study, a heuristic based genetic algorithm modeled to solve cluster
TSP. Here we developed an alternative methodology, i.e., heuristic to the creation
and re-linking the inter-cluster and used GA for optimizing the path in intra-
cluster also. Finally, an optimized path is generated. Again different numbers of
the cluster are investigated because of such realistic happening found in the small
scale tourism industry. In the tourism industry, it oftenly found that a different
number of sight scenery are the demand by every group of tourist. Since tourism
is travel for pleasure and business, so management prepares different kinds of
travel plan in that case such proposed cluster model effectively works. Without
cluster attempt to solve such TSP using a heuristic process like using GA, is
a big headache regarding CPU time and complexity. The main motto of our
prescribed article is to demonstrate the efficiency of our proposed cluster TSP
algorithm than any other conventional Genetic Algorithms. We got a set of the
heuristic solution by applying our proposed GA on CTSP. The effectiveness of
clustering method has been examined with both lexisearch algorithm (LSA) and
OCTSP [2] for few small TSPLIB instances. The experiment shows that CTSP
is better than LSA and HGA also. Few TSPLIB instances also compared with
HGA and the overall result is good enough. In the future, we can extend the
algorithm using fuzzy distance for cluster creation and dynamic relinking of the
inter-cluster also.
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Proof
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