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Convalescing Cluster Configuration Using a Superlative Framework
2015
The Scientific World Journal
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Data clustering is one such descriptive data mining technique which guides in partitioning data objects into disjoint segments.K-means algorithm is a versatile algorithm among the various approaches used ...
The specific feature of the proposed algorithm is discretizing the dataset, thereby improving the accuracy of clustering, and also adopting the binary search initialization method to generate cluster centroids ...
Conflict of Interests The authors proclaim that there is no conflict of interests concerning the publication of this paper. ...
doi:10.1155/2015/180749
pmid:26543895
pmcid:PMC4620246
fatcat:2eipz23zara6fkralxcxupocd4
A New Hybrid Clustering Method of Binary Differential Evolution and Marine Predators Algorithm for Multi-omics Datasets
2021
International Journal of Intelligent Engineering and Systems
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Another tricky problem in data clustering is determining the best number of clusters used by a clustering algorithm. ...
Due to the big success of metaheuristics in solving the automatic clustering problems, we propose in this paper a new hybrid method that utilizes two powerful metaheuristics algorithms, the Binary Differential ...
Conflicts of Interest The authors declare no conflict of interest. ...
doi:10.22266/ijies2021.0430.38
fatcat:hong55pemvajbbf62bcewqk2sq
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS
[article]
2021
arXiv
pre-print
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First, a calibrated clustering analysis of the search space is performed. Second, the centroids are extracted and used to initialize a NAS algorithm. ...
In this study, we propose to accelerate a NAS algorithm using a data-driven initialization technique, leveraging the availability of NAS benchmarks. Particularly, we proposed a two-step methodology. ...
)" and Helmholtz Excellent Professorship "Data Science in Earth Observation -Big Data Fusion for Urban Research"(W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework ...
arXiv:2108.09126v1
fatcat:gnhoukif6bah7haumofmtfr57u
A New Approach of Dynamic Clustering Based on Particle Swarm Optimization and Application in Image Segmentation
2017
Computing and informatics
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This paper presents a new approach of dynamic clustering based on improved Particle Swarm Optimization (PSO) and which is applied to image segmentation (called DCPSONS). ...
Experimental results in using sixteen benchmark data sets and several images of synthetic and natural benchmark data demonstrate that the proposed DCPSONS algorithm substantially outperforms other competitive ...
Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 61070008 and 61364025). ...
doi:10.4149/cai_2017_3_637
fatcat:5e7hov3lrfeqjfecyjeelbului
Fast Exact k-Means, k-Medians and Bregman Divergence Clustering in 1D
[article]
2018
arXiv
pre-print
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Moreover, we show how to reduce the space usage for some of them, as well as generalize them to data structures that can quickly report an optimal k-Means clustering for any k. ...
The k-Means clustering problem on n points is NP-Hard for any dimension d> 2, however, for the 1D case there exists exact polynomial time algorithms. ...
In conclusion a binary search on λ ends when the algorithm probes a value where the the number of clusters possible in the optimal regularized clustering contains k. ...
arXiv:1701.07204v4
fatcat:3hsjmr2p5ba3hj3yu3spldy2mi
A Data-driven Approach to Neural Architecture Search Initialization
[article]
2021
arXiv
pre-print
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First, we perform a calibrated clustering analysis of the search space, and second, we extract the centroids and use them to initialize a NAS algorithm. ...
Algorithmic design in neural architecture search (NAS) has received a lot of attention, aiming to improve performance and reduce computational cost. ...
One might also explore the benefits of such data-driven initialization method on other families of algorithms (Bayesian optimization, local search, etc.). ...
arXiv:2111.03524v1
fatcat:scr4ii7oljhejg7xtffnkaqvh4
Thick boundaries in binary space and their influence on nearest-neighbor search
2012
Pattern Recognition Letters
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In the case of binary spaces, the thick boundaries of the Voronoi diagram influence the search regardless of the data dimensionality, as we explain in details in Section 3. ...
This violates the implicit assumption made by most ANN algorithms that points can be neatly assigned to clusters centered around a set of cluster centers. ...
Instead of building a tree by recursively splitting in half the data that reach each node, it uses the k-means algorithm to split it into k clusters. ...
doi:10.1016/j.patrec.2012.08.006
fatcat:3znq765zt5gl7omss2khpy4zbm
Grey Wolf Optimization Applied to the 0/1 Knapsack Problem
2017
International Journal of Computer Applications
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A novel algorithm is proposed in order to find the best solution that maximizes the total carried value without exceeding a known capacity using Grey Wolf Optimization (GWO) and K-means clustering algorithms ...
K-means clustering algorithm is used to group each 5-12 agents with each other at one cluster according to GWO constraint. The evaluated performance is satisfying. ...
Grey wolf optimizer (GWO) is a new optimizer being used to solve some problems and produced good results; thus, researchers in this paper search to find the applicability of GWO on solving 01KP problem ...
doi:10.5120/ijca2017914734
fatcat:vngjsqy7dbdt7lve5nebbqltta
Classification with binary gene expressions
2009
Journal of Biomedical Science and Engineering
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In this paper we show how properties of binary spaces can be useful in making inferences from microarray data. ...
We further show preliminary results that working with binary data considerably reduces variability in the results across choice of algorithms in the pre-processing stages of microarray analysis. ...
In (a) spectral clustering is applied to continuous data by using Euclidean distance, in (b) binary data is used with Euclidean distance and in (c) binary data is used with Tanimoto coefficient for spectral ...
doi:10.4236/jbise.2009.26056
fatcat:sffu4fih4zb7tez6kzkiipyjdm
Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions
2021
Symmetry
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The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters ...
To solve these problems, the whale optimization algorithm is used. ...
algorithm based on the whale optimization algorithm/K-means algorithm; an informative feature selection algorithm using the binary WOA; a fuzzy classifier parameters optimization algorithm based on the ...
doi:10.3390/sym13071211
fatcat:6bjjwszdprbmfjna5j6kbcecku
Feature Selection using Genetic Algorithm for Clustering high Dimensional Data
2018
International Journal of Engineering & Technology
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uses genetic algorithm for searching an effective feature subspace in a large feature space. ...
One of the open problems of modern data mining is clustering high dimensional data. For this in the paper a new technique called GA-HDClustering is proposed, which works in two steps. ...
Binary encoding use a search space which is larger than floating point encoding, but the crossover and mutation operation can perform more easily on it. So, we use binary encoding for this paper. ...
doi:10.14419/ijet.v7i2.11.11001
fatcat:5wgzmisvyrfi5njemiabhprv6e
Quantum algorithm for finding minimum values in a Quantum Random Access Memory
[article]
2023
arXiv
pre-print
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Furthermore, we demonstrate how the proposed algorithm would be used in an unsupervised machine learning task through a quantum version of the K-means algorithm. ...
However, the optimal classical deterministic algorithm can find the minimum value with a time complexity that grows linearly with the number of elements in the database. ...
It can be built using an equivalent quantum circuit in which classical data is stored in a quantum register in binary form. ...
arXiv:2301.05122v1
fatcat:jbp4vmvlq5cj3aev3qoshbnp74
Randomised Local Search Algorithm for the Clustering Problem
2000
Pattern Analysis and Applications
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We introduce a new randomised local search algorithm for the clustering problem. The algorithm is easy to implement, sufficiently fast, and competitive with the best clustering methods. ...
Instead, the best clustering results have been obtained by more complex techniques such as tabu search and genetic algorithms at the cost of high run time. ...
This is formalised in the following two optimality conditions [3] : ¼ Nearest neighbour condition: for a given set of cluster centroids, any data object can be optimally classified by assigning it to ...
doi:10.1007/s100440070007
fatcat:2uu2ne6hijgdjnh22dsyig447y
A New Approach for Data Clustering Based on PSO with Local Search
2008
Computer and Information Science
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In this paper a modification strategy is proposed for the particle swarm optimization (PSO) algorithm and applied in the data sets. ...
The term "clustering" is used in several research communities to describe methods for grouping of unlabeled data. ...
In this study, a data clustering algorithm based on Simple PSO, Roulette Wheel Selection and K-Means algorithm. ...
doi:10.5539/cis.v1n4p139
fatcat:2jmdjosl2nctpbdqaedbazfpfq
Design of hybrids for the minimum sum-of-squares clustering problem
2003
Computational Statistics & Data Analysis
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A series of metaheuristic algorithms is proposed and analyzed for the non-hierarchical clustering problem under the criterion of minimum Sum-of-Squares Clustering. ...
A series of computational experiments has been performed. The proposed algorithms obtain better results than previously reported methods, especially with small numbers of clusters. ...
Next, we design a simple Tabu Search algorithm that uses the neighboring moves employed in K-Means. These moves consist at each step in the movement of an entity from a cluster to a different one. ...
doi:10.1016/s0167-9473(02)00224-4
fatcat:ykb2m2xmtjg7fc3vmagfepllai
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