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检索条件"主题词=clustering algorithms"

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clustering short text and its evaluation

Clustering short text and its evaluation

引用

13th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2012

作者： Shrestha, Prajol Jacquin, Christine Daille, Béatrice Université de Nantes Nantes Cedex 3 44322 France

ISBN: (纸本)9783642286001

Recently there has been an increase in interest towards clustering short text because it could be used in many NLP applications. According to the application, a variety of short text could be defined mainly in terms of their length (e.g. sentence, paragraphs) and type (e.g. scientific papers, newspapers). Finding a clustering method that is able to cluster short text in general is difficult. In this paper, we cluster 4 different corpora with different types of text with varying length and evaluate them against the gold standard. Based on these clustering experiments, we show how different similarity measures, clustering algorithms, and cluster evaluation methods effect the resulting clusters. We discuss four existing corpus based similarity methods, Cosine similarity, Latent Semantic Analysis, Short text Vector Space Model, and Kullback-Leibler distance, four well known clustering methods, Complete Link, Single Link, Average Link hierarchical clustering and Spectral clustering, and three evaluation methods, clustering F-measure, adjusted Rand Index, and V. Our experiments show that corpus based similarity measures do not significantly affect the clusters and that the performance of spectral clustering is better than hierarchical clustering. We also show that the values given by the evaluation methods do not always represent the usability of the clusters. © 2012 Springer-Verlag.

关键词： clustering algorithms

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clustering algorithm based on improved particle swarm optimization

引用

2013 International Conference on Advances in Materials Science and Manufacturing Technology, AMSMT 2013

作者： Wang, Hong Chun Wen, Feng Wen Song, Feng Department of Mathematics Chongqing Normal University Chongqing 401331 China

ISBN: (纸本)9783037857984

K-means algorithm has therefore become one of the methods widely used in cluster analysis. But the classification results of K-means algorithm depend on the initial cluster centers choice. We present a new neighborhood for PSO methods called the area of influence (AOI) and consider the combination of K-means has strong capacity of local searching and PSO has power global search ability. The improved PSO, i.e., improves the K-mean's local searching capacity, accelerates the convergence rate, and prevents the premature convergence effectively. © (2013) Trans Tech Publications, Switzerland.

关键词： clustering algorithms

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Personalized Federated Learning via Convex clustering 8

Personalized Federated Learning via Convex Clustering

引用

8th IEEE International Smart Cities Conference, ISC2 2022

作者： Armacki, Aleksandar Bajovic, Dragana Jakovetic, Dusan Kar, Soummya Carnegie Mellon University PittsburghPA United States University of Novi Sad Faculty of Technical Sciences Novi Sad Serbia University of Novi Sad Faculty of Sciences Novi Sad Serbia

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

We propose a parametric family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized via a sum-of-norms penalty, weighted by a penalty parameter A. The proposed approach enables 'automatic' model clustering, without prior knowledge of the hidden cluster structure, nor the number of clusters. Analytical bounds on the weight parameter, that lead to simultaneous personalization, generalization and automatic model clustering are provided. The solution to the formulated problem enables personalization, by providing different models across different clusters, and generalization, by providing models different than the per-user models computed in isolation. We then provide an efficient algorithm based on the Parallel Direction Method of Multipliers (PDMM) to solve the proposed formulation in a federated server-users setting. Numerical experiments corroborate our findings. As an interesting byproduct, our results provide several generalizations to convex clustering. © 2022 IEEE.

关键词： Analytical models Costs Federated learning Smart cities Computational modeling clustering algorithms Stochastic processes

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Multi-prototype local density-based hierarchical clustering

Multi-prototype local density-based hierarchical clustering

引用

International Joint Conference on Neural Networks, IJCNN 2015

作者： Da Silva, Leonardo Enzo Brito Wunsch, Donald C. Applied Computational Intelligence Laboratory Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9781479919604

In this paper, novel hierarchical clustering algorithms, Growing Fuzzy ART (GFA) and Self-Resonant Growing Fuzzy ART (SRGFA), based on connecting prototypes, are presented. The prototypes are generated by vector quantization algorithms: K-means, Self-Organizing Maps, and Fuzzy ART. The Euclidean distance is used to train the first two algorithms in order to allocate the centroids and neurons, respectively. The latter uses fuzzy set operations to check resonance and learn the categories. For each method, a subset of the data set is associated with each prototype;this subset consists of all patterns that, according to a similarity measure, are within a certain threshold from a given prototype. In the case of K-means and Self-Organizing Map, the region is a hypersphere, and in the case of Fuzzy ART, it is a hyperbox. In order to relax the similarity constraint and create larger subsets of data for each prototype, the values of the Euclidean norm and the vigilance parameter are continuously increased and decreased, respectively, according to a step size. Prototypes that have patterns in common are linked together in the process. The data set's final partition is selected as the clustering state in which the algorithm spent most of its time. Synthetic and real world data sets are used to depict the experimental results. External validity indices are used as figures of merit to evaluate the quality of the final partitions. © 2015 IEEE.

关键词： clustering algorithms

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A two level co-clustering algorithm for very large data sets 18

A two level co-clustering algorithm for very large data sets

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18e Extraction et Gestion des Connaissances, EGC 2018 - 18th Conference on Knowledge Extraction and Management, EGC 2018

作者： Marius, Bartcus Marc, Boullé Fabrice, Clérot Orange Labs

ISBN: (纸本)9791096289073

Co-clustering is a data mining technique that aims at identifying the underlying structure between the rows and the columns of a data matrix in the form of homogeneous blocks. It has many real world applications, however many current co-clustering algorithms are not suited on large data sets. One of the successfully used approach to co-cluster large data sets is the MODL co-clustering method that optimizes a criterion based on a regularized likelihood. However, difficulties are encountered with huge data sets. In this paper, we present a new two-level co-clustering algorithm, given the MODL criterion allowing to efficiently deal with very large data sets that does not fit in memory. Our experiments, on both simulated and real world data, show that the proposed approach dramatically reduces the computation time without significantly decreasing the quality of the co-clustering solution. © 2018 Extraction et Gestion des Connaissances, EGC 2018. All Rights Reserved.

关键词： clustering algorithms

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A fuzzy subspace algorithm for clustering high dimensional data

引用

2nd International Conference on Advanced Data Mining and Applications, ADMA 2006

作者： Gan, Guojun Wu, Jianhong Yang, Zijiang Department of Mathematics and Statistics York University TorontoONM3J 1P3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada

ISBN: (纸本)3540370250

In fuzzy clustering algorithms each object has a fuzzy membership associated with each cluster indicating the degree of association of the object to the cluster. Here we present a fuzzy subspace clustering algorithm, FSC, in which each dimension has a weight associated with each cluster indicating the degree of importance of the dimension to the cluster. Using fuzzy techniques for subspace clustering, our algorithm avoids the difficulty of choosing appropriate cluster dimensions for each cluster during the iterations. Our analysis and simulations strongly show that FSC is very efficient and the clustering results produced by FSC are very high in accuracy. © Springer-Verlag Berlin Heidelberg 2006.

关键词： clustering algorithms

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Face clustering Algorithm Based on Domain Structural Similarity 3

Face Clustering Algorithm Based on Domain Structural Similar...

引用

3rd International Conference on Computer Science and Management Technology, ICCSMT 2022

作者： Liu, Yuning Fan, Bingbing South China Normal University 1 Guangzhou China

ISBN: (纸本)9798350320404

A KNN-Struct-ChineseWhispers algorithm based on K-nearest neighbour similarity aggregation is proposed for face clustering to address the problem that the ChineseWhispers algorithm is slow for large-scale face clustering and noisy edges lead to reduced clustering accuracy. Using Faiss to speed up the K-nearest neighbour graph initialisation process, the problem of calculating the similarity between two nodes is transformed into a limited radius K neighbour query for a single node corresponding to the indexed clustering centre. The noisy edges in the graph are filtered by aggregating feature similarity through the neighbourhood structure, and an isolated node detection module is added to improve computational efficiency and clustering accuracy. Based on the CNN+ArcFace Loss method for extracting face features, experiments on several benchmark datasets show that the proposed algorithm can effectively improve the time efficiency and accuracy of clustering compared to existing face clustering algorithms. © 2022 IEEE.

关键词： clustering algorithms

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Spatial correleration clustering algorithm for data gathering in wireless sensor networks

Spatial correleration clustering algorithm for data gatherin...

引用

2nd International Conference on Networks Security, Wireless Communications and Trusted Computing, NSWCTC 2010

作者： Hao, Jutao Zhao, Jingjing School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Electric Power and Automation Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9780769540115

Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many-to-one traffic pattern. These three characteristics may give rise to funneling effects that can lead to severe packet collision, network congestion, packet loss and even congestion collapse. This can also result in hotspots of energy consumption that may cause premature death of sensor nodes and even premature death of entire network. In this paper, exploiting spatial correlation of nodes to form clusters of nodes sensing similar values, and only cluster head sensor reading is transmit to sink, such can efficiently alleviates the funneling effects. A novelty clustering algorithm is proposed which can greatly reduce the number of cluster heads. Experimental results validate the effectiveness of this approach. © 2010 IEEE.

关键词： clustering algorithms

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Table based single pass algorithm for clustering news articles in ***

Table based single pass algorithm for clustering news articl...

引用

International Conference on Computational Science and Its Applications, ICCSA 2008

作者： Yeom, Gwyduk Jo, Taeho Yeom, Youngik School of Information and Communication Engineering Sungkyunkwan University 300 Cheoncheon Dong Suwon Gyeonggi 440-746 Korea Republic of School of Information and Information Engineering Inha University

ISBN: (纸本)354069840X

This research proposes a modified version of single pass algorithm specialized for text clustering. Encoding documents into numerical vectors for using the traditional version of single pass algorithm causes the two main problems: huge dimensionality and sparse distribution. Therefore, in order to address the two problems, this research modifies the single pass algorithm into its version where documents are encoded into other forms than numerical vectors. In the proposed version, documents are mapped into tables and an operation on two tables is defined for using the single pass algorithm. The goal of this research is to improve the performance of single pass algorithm for text clustering by modifying it. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： clustering algorithms

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Using clustering to improve the structure of natural language requirements documents 1

引用

19th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2013

作者： Ferrari, Alessio Gnesi, Stefania Tolomei, Gabriele ISTI-CNR Pisa Italy DAIS Università Ca' Foscari Venezia Italy

ISBN: (数字)9783642374227

ISBN: (纸本)9783642374210

[Context and motivation] System requirements are normally provided in the form of natural language documents. Such documents need to be properly structured, in order to ease the overall uptake of the requirements by the readers of the document. A structure that allows a proper understanding of a requirements document shall satisfy two main quality attributes: (i) requirements relatedness: each requirement is conceptually connected with the requirements in the same section;(ii) sections independence: each section is conceptually separated from the others. [Question/Problem] Automatically identifying the parts of the document that lack requirements relatedness and sections independence may help improve the document structure. [Principal idea/results] To this end, we define a novel clustering algorithm named Sliding Head-Tail Component (S-HTC). The algorithm groups together similar requirements that are contiguous in the requirements document. We claim that such algorithm allows discovering the structure of the document in the way it is perceived by the reader. If the structure originally provided by the document does not match the structure discovered by the algorithm, hints are given to identify the parts of the document that lack requirements relatedness and sections independence. [Contribution] We evaluate the effectiveness of the algorithm with a pilot test on a requirements standard of the railway domain (583 requirements). © 2013 Springer-Verlag.

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