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NEW ALGORITHMS FOR SOLVING THE FUZZY CLUSTERING PROBLEM
Published 2020“…The performance of the new algorithms is compared with the fuzzy c-means algorithm by testing them on four published data sets. …”
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Correlation Clustering with Overlaps
Published 2020“…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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Mapping realistic data sets on parallel computers
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Computational Experience On Four Algorithms For The Hard Clustering Problem
Published 2020“…In this paper, we study the four algorithms and compare their computational performance for the clustering problem. …”
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A new approach to record clustering for large databases. (c1997)
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An enhanced k-means clustering algorithm for pattern discovery in healthcare data
Published 2015“…Data mining approaches offer the methodology and technology to transform these heterogeneous data into meaningful information for decision making. This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. …”
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Automatic keyword extraction from a real estate classifieds data set
Published 2011“…Keywords are considered to be a solution to this problem and are now widely used to search the information over the internet.In this project we analyze a real estate classifieds data set, with an objective to find keywords that represent this data set. …”
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Data reductions and combinatorial bounds for improved approximation algorithms
Published 2016“…Kernelization algorithms in the context of Parameterized Complexity are often based on a combination of data reduction rules and combinatorial insights. …”
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Allocating data to distributed-memory multiprocessors by genetic algorithms
Published 2016“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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Variable Selection in Data Analysis: A Synthetic Data Toolkit
Published 2024“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering
Published 2024“…Finally, this study proposed a novel multi-view learning framework that analyzes multi-source data and generates fine clusters efficiently.…”
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Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing
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doctoralThesis -
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Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Published 1992“…We also present a technique for efficient mapping of large data sets. The algorithms include a parallel genetic algorithm (PGA), a parallel neural network algorithm (PNN) and a parallel simulated annealing algorithm (PSA). …”
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Hardening the ElGamal cryptosystem in the setting of the second group of units. (c2011)
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DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data
Published 2022“…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
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Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
Published 2021“…This work investigates the problem of scheduling the processing of tasks with non-identical sizes and different priorities on a set of parallel processors. An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
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Correlation Clustering via 2-Club Clustering with Vertex Splitting
Published 2024“…In the realm of graph theory, correlation cluster is studied as a graph modification problem known under "Cluster Editing." …”
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masterThesis