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A Survey of Data Clustering Techniques
Published 2023Subjects: “…Cluster analysis -- Data processing…”
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Clustering/partitioning algorithms and comparative analysis
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masterThesis -
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A Novel Iterative non-Deterministic Algorithm for the Data Clustering Problem
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An enhanced k-means clustering algorithm for pattern discovery in healthcare data
Published 2015“…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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DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data
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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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Improved search-tree algorithms for the cluster edit problem. (c2011)
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Correlation Clustering via s-Club Cluster Edge Deletion
Published 2023Subjects: “…Cluster analysis -- Data processing…”
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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
Published 2022Subjects: “…Data clustering…”
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A SIMULATED ANNEALING ALGORITHM FOR THE CLUSTERING PROBLEM
Published 2020“…In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. …”
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NEW ALGORITHMS FOR SOLVING THE FUZZY CLUSTERING PROBLEM
Published 2020“…Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is proved. …”
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(k, l)-Clustering for Transactional Data Streams Anonymization
Published 2018“…We evaluate our algorithm in terms of utility by considering two different (k, l)-clustering approaches.…”
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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…Among these, K-means is widely used for efficiently solving clustering problems. This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …”
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Distributed dimension reduction algorithms for widely dispersed data
Published 2002“…It is well known that information retrieval, clustering and visualization can often be improved by reducing the dimensionality of high dimensional data. …”
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