Results 11 to 20 of about 225,995 (267)
Applied Sciences, 2021 With the frequent occurrence of network security events, the intrusion detection system will generate alarm and log records when monitoring the network environment in which a large number of log and alarm records are redundant, which brings great burden ...
Leiting Wang, Lize Gu, Yifan Tang
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Contrastive Hierarchical Clustering
, 2023 Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the information contained in the flat partition is limited.
Znalezniak, Michał +4 more
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Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering. [PDF]
PLoS ONE, 2015 Agglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments.
Lerato Lerato, Thomas Niesler
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Robust Hierarchical Clustering
CoRR, 2013 One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret.
Maria-Florina Balcan +2 more
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Multi-attribute Hierarchical Clustering for Product Family Division of Customized Wooden Doors
BioResources, 2023 To improve the production system for customized wooden doors and to gain research and development efficiency, this paper proposed the feasibility of using hierarchical clustering algorithms to cluster a company's customized wooden door products and its ...
Na Zhang, Wei Xu, Yong Tan
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Relation between financial market structure and the real economy: comparison between clustering methods. [PDF]
PLoS ONE, 2015 We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification.
Nicoló Musmeci +2 more
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CoRR, 2020 As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering. In this paper we extend this notion to hierarchical clustering, where the goal
Sara Ahmadian +8 more
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IEEE Access, 2022 Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma.
Naoki Masuyama +4 more
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Towards semi-supervised ensemble clustering using a new membership similarity measure
Automatika, 2023 Hierarchical clustering is a common type of clustering in which the dataset is hierarchically divided and represented by a dendrogram. Agglomerative Hierarchical Clustering (AHC) is a common type of hierarchical clustering in which clusters are created ...
Wenjun Li, Ting Li, Musa Mojarad
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Renyi entropy driven hierarchical graph clustering [PDF]
PeerJ Computer Science, 2021 This article explores a graph clustering method that is derived from an information theoretic method that clusters points in ${{\mathbb{R}}^{n}}$Rn relying on Renyi entropy, which involves computing the usual Euclidean distance between these points.
Frédérique Oggier, Anwitaman Datta
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