Overlapping Multi-hop Clustering for Wireless Sensor Networks [PDF]
, 2009 Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a
Younis, Mohamed +2 more
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An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity
IEEE Access, 2019 Spectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity
Lijuan Wang, Shifei Ding, Hongjie Jia
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Unifying Sparsest Cut, Cluster Deletion, and Modularity Clustering Objectives with Correlation Clustering [PDF]
, 2018 Graph clustering, or community detection, is the task of identifying groups of closely related objects in a large network. In this paper we introduce a new community-detection framework called LambdaCC that is based on a specially weighted version of ...
Gleich, David +2 more
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Constrained Clustering Problems [PDF]
, 1998 In the paper a view, based on the optimization approach, is given on different types of constrained clustering problems and methods for their solution.
Vladimir Batagelj, Anuška Ferligoj
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Clustering benchmark datasets exploiting the fundamental clustering problems
Data in Brief, 2020 The Fundamental Clustering Problems Suite (FCPS) offers a variety of clustering challenges that any algorithm should be able to handle given real-world data. The FCPS consists of datasets with known a priori classifications that are to be reproduced by the algorithm.
Michael C. Thrun, Alfred Ultsch
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Wind and Photovoltaic Generation Scene Division Based on Improved K-means Clustering
发电技术, 2020 In view of the uncertainty of power generation in renewable energy, especially wind power and photovoltaic power generation, the improved K-means clustering method was used to segment the state of power generation.
Xuewei SONG, Yuyao LIU
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Sparse Subspace Learning Based on Learnable Constraints for Image Clustering
IEEE Access, 2023 Sparse subspace clustering is a widely used method for clustering high dimensional data, but the traditional method is complex and requires prior information that can be difficult to obtain in unsupervised scenarios.
Siyuan Zhao
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Optimal clustering of frequency-constrained maintenance jobs with shared set-ups [PDF]
, 1997 Since maintenance jobs often require one or more set-up activities, joint execution or clustering of maintenance jobs is a powerful instrument to reduce shut-down costs.
Dijkhuizen, Gerhard van +1 more
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Research on improved region division method in underground WLAN location fingerprints positioning
Gong-kuang zidonghua, 2020 Underground WLAN location fingerprinting personnel positioning system mainly realizes overall division of location fingerprinting samples through clustering algorithm, but existing clustering algorithm only carries out the clustering division according ...
SONG Mingzhi +2 more
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An Improved Three-Way Clustering Based on Ensemble Strategy
Mathematics, 2022 As a powerful data analysis technique, clustering plays an important role in data mining. Traditional hard clustering uses one set with a crisp boundary to represent a cluster, which cannot solve the problem of inaccurate decision-making caused by ...
Tingfeng Wu, Jiachen Fan, Pingxin Wang
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