Статті в журналах: "Clustering based on correlation"

1

Hua, Jialin, Jian Yu, and Miin-Shen Yang. "Star-based learning correlation clustering." Pattern Recognition 116 (August 2021): 107966. http://dx.doi.org/10.1016/j.patcog.2021.107966.

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2

Pandove, Divya, Rinkle Rani, and Shivani Goel. "Local graph based correlation clustering." Knowledge-Based Systems 138 (December 2017): 155–75. http://dx.doi.org/10.1016/j.knosys.2017.09.034.

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3

Sato-Ilic, Mika. "On Fuzzy Clustering based Correlation." Procedia Computer Science 12 (2012): 230–35. http://dx.doi.org/10.1016/j.procs.2012.09.061.

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4

Zhu, Guibo, Jinqiao Wang, and Hanqing Lu. "Clustering based ensemble correlation tracking." Computer Vision and Image Understanding 153 (December 2016): 55–63. http://dx.doi.org/10.1016/j.cviu.2016.05.006.

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5

Rao .S, Venkata. "Correlation Preserving Indexing Based Text Clustering." IOSR Journal of Computer Engineering 13, no. 1 (2013): 27–30. http://dx.doi.org/10.9790/0661-1312730.

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6

Pandove, Divya, Shivani Goel, and Rinkle Rani. "General correlation coefficient based agglomerative clustering." Cluster Computing 22, no. 2 (November 2, 2018): 553–83. http://dx.doi.org/10.1007/s10586-018-2863-y.

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7

Hua, Jia-Lin, Jian Yu, and Miin-Shen Yang. "Correlative Density-Based Clustering." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6935–43. http://dx.doi.org/10.1166/jctn.2016.5650.

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Mountains, which heap up by densities of a data set, intuitively reflect the structure of data points. These mountain clustering methods are useful for grouping data points. However, the previous mountain-based clustering suffers from the choice of parameters which are used to compute the density. In this paper, we adopt correlation analysis to determine the density, and propose a new clustering algorithm, called Correlative Density-based Clustering (CDC). The new algorithm computes the density with a modified way and determines the parameters based on the inherent structure of data points. Experiments on artificial datasets and real datasets demonstrate the simplicity and effectiveness of the proposed approach.

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8

Jain, Aaditya, and Suchita Tyagi. "Priority Based New Approach for Correlation Clustering." International Journal of Information Technology and Computer Science 9, no. 3 (March 8, 2017): 71–79. http://dx.doi.org/10.5815/ijitcs.2017.03.08.

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9

Sudhher, P. "Clustering Algorithm Based On Correlation Preserving Indexing." IOSR Journal of Computer Engineering 15, no. 3 (2013): 58–63. http://dx.doi.org/10.9790/0661-1535863.

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10

Chiou, Jeng-Min, and Pai-Ling Li. "Correlation-Based Functional Clustering via Subspace Projection." Journal of the American Statistical Association 103, no. 484 (December 2008): 1684–92. http://dx.doi.org/10.1198/016214508000000814.

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11

Dutta, Ayan, Emily Czarnecki, Vladimir Ufimtsev, and Asai Asaithambi. "Correlation clustering-based multi-robot task allocation." ACM SIGAPP Applied Computing Review 19, no. 4 (January 28, 2020): 5–16. http://dx.doi.org/10.1145/3381307.3381308.

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12

Lee, Taehoon, Seung Jean Kim, Eui-Young Chung, and Sungroh Yoon. "K-maximin clustering: a maximin correlation approach to partition-based clustering." IEICE Electronics Express 6, no. 17 (2009): 1205–11. http://dx.doi.org/10.1587/elex.6.1205.

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13

Costa, António C., J. A. Tenreiro Machado, and Maria Dulce Quelhas. "Multidimensional Scaling Applied to Histogram-Based DNA Analysis." Comparative and Functional Genomics 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/289694.

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This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendallτrank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

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14

ZHANG, Hong, Fei WU, and Xiao-Long ZHANG. "Multimedia Data Clustering Based on Correlation Matrix Fusion." Chinese Journal of Computers 34, no. 9 (October 15, 2011): 1705–11. http://dx.doi.org/10.3724/sp.j.1016.2011.01705.

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15

TU, Li, Ling CHEN, and Ling-Jun ZOU. "Clustering Multiple Data Streams Based on Correlation Analysis." Journal of Software 20, no. 7 (March 10, 2010): 1756–67. http://dx.doi.org/10.3724/sp.j.1001.2009.00566.

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16

Onnela, J. P., K. Kaski, and J. Kert�sz. "Clustering and information in correlation based financial networks." European Physical Journal B - Condensed Matter 38, no. 2 (March 1, 2004): 353–62. http://dx.doi.org/10.1140/epjb/e2004-00128-7.

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17

Fujiwara, Koichi, Manabu Kano, and Shinji Hasebe. "Correlation-based spectral clustering for flexible process monitoring." Journal of Process Control 21, no. 10 (December 2011): 1438–48. http://dx.doi.org/10.1016/j.jprocont.2011.06.023.

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18

Achtert, Elke, Christian Böhm, Jörn David, Peer Kröger, and Arthur Zimek. "Global Correlation Clustering Based on the Hough Transform." Statistical Analysis and Data Mining: The ASA Data Science Journal 1, no. 3 (November 3, 2008): 111–27. http://dx.doi.org/10.1002/sam.10012.

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19

Xu, Yan, Jiangtao Dong, Zishuo Han, and Peiguang Wang. "Multichannel Correlation Clustering Target Detection." Information Technology And Control 49, no. 3 (September 23, 2020): 335–45. http://dx.doi.org/10.5755/j01.itc.49.3.25507.

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During target tracking, certain multi-modal background scenes are unsuitable for off-line training model. To solve this problem, based on the Gaussian mixture model and considering the pixels’ time correlation, a method that combines the random sampling operator and neighborhood space propagation theory is proposed to simplify the model update process. To accelerate the model convergence, the observation vector is constructed in the time dimension by optimizing the model parameters. Finally, a three channel-multimodal background model fusing the HSI color space and gradient information is established in this study. Hence the detection of moving targets in a complicated environment is achieved. Experiments indicate that the algorithm has good detection performance when inhibiting ghosts, dynamic background, and shade; thus, the execution efficiency can meet the needs of real-time computing.

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20

Wang, Wenguang, Wenhong Wang, and Hongfu Liu. "Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection." Remote Sensing 14, no. 5 (February 26, 2022): 1156. http://dx.doi.org/10.3390/rs14051156.

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Hyperspectral band selection is a commonly used technique to alleviate the curse of dimensionality. Recently, clustering-based methods have attracted much attention for their effectiveness in selecting informative and representative bands. However, the single clustering algorithm is used in most of the clustering-based methods, and the neglect of the correlation among adjacent bands in their clustering procedure is prone to resulting in the degradation of the representativeness of the selected band set. This may, consequently, adversely impact hyperspectral classification performance. To tackle such issues, in this paper, we propose a correlation-guided ensemble clustering approach for hyperspectral band selection. By exploiting ensemble clustering, more effective clustering results are expected based on multiple band partitions given by base clustering with different parameters. In addition, given that adjacent bands are most probably located in the same cluster, a novel consensus function is designed to construct the final clustering partition by performing an agglomerative clustering. Thus, the performance of our addressed task (band selection) is further improved. The experimental results on three real-world datasets demonstrate that the performance of our proposed method is superior compared with those of state-of-the-art methods.

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21

Aslam, Mubeen, Lukman bin AB Rahim, Junzo Watada, and Manzoor Hashmani. "Clustering-Based Cloud Migration Strategies." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 3 (May 20, 2018): 295–305. http://dx.doi.org/10.20965/jaciii.2018.p0295.

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The k-means algorithm of the partitioning clustering method is used to analyze cloud migration strategies in this study. The extent of assistance required to be provided to organizations while working on migration strategies was investigated for each cloud service model and concrete clusters were formed. This investigation is intended to aid cloud consumers in selecting their required cloud migration strategy. It is not easy for businessmen to select the most appropriate cloud migration strategy, and therefore, we proposed a suitable model to solve this problem. This model comprises a web of migration strategies, which provides an unambiguous visualization of the selected migration strategy. The cloud migration strategy targets the technical aspects linked with cloud facilities and measures the critical realization factors for cloud acceptance. Based on similar features, a correlation among the migration strategies is suggested, and three main clusters are formed accordingly. This helps to link the cloud migration strategies across the cloud service models (software as a service, platform as a service, and infrastructure as a service). This correlation was justified using the digital logic approach. This study is useful for the academia and industry as the proposed migration strategy selection process aids cloud consumers in efficiently selecting a cloud migration strategy for their legacy applications.

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22

Ahn, Kook Jin, Graham Cormode, Sudipto Guha, Andrew McGregor, and Anthony Wirth. "Correlation Clustering in Data Streams." Algorithmica 83, no. 7 (March 13, 2021): 1980–2017. http://dx.doi.org/10.1007/s00453-021-00816-9.

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AbstractClustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as k-center, k-median, and k-means. Such algorithms need to be both time and and space efficient. In this paper, we address the problem of correlation clustering in the dynamic data stream model. The stream consists of updates to the edge weights of a graph on n nodes and the goal is to find a node-partition such that the end-points of negative-weight edges are typically in different clusters whereas the end-points of positive-weight edges are typically in the same cluster. We present polynomial-time, $$O(n\cdot {{\,\mathrm{polylog}\,}}n)$$ O ( n · polylog n ) -space approximation algorithms for natural problems that arise. We first develop data structures based on linear sketches that allow the “quality” of a given node-partition to be measured. We then combine these data structures with convex programming and sampling techniques to solve the relevant approximation problem. Unfortunately, the standard LP and SDP formulations are not obviously solvable in $$O(n\cdot {{\,\mathrm{polylog}\,}}n)$$ O ( n · polylog n ) -space. Our work presents space-efficient algorithms for the convex programming required, as well as approaches to reduce the adaptivity of the sampling.

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23

Yeo, Myungho. "Data Correlation-Based Clustering Algorithm in Wireless Sensor Networks." KSII Transactions on Internet and Information Systems 3, no. 3 (June 22, 2009): 331–43. http://dx.doi.org/10.3837/tiis.2009.03.007.

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24

Shin, Moon-Sun, Ho-Sung Moon, Keun-Ho Ryu, and Jong-Su Jang. "Alert Correlation Analysis based on Clustering Technique for IDS." KIPS Transactions:PartC 10C, no. 6 (October 1, 2003): 665–74. http://dx.doi.org/10.3745/kipstc.2003.10c.6.665.

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25

Zhang, Guixin, and Zhenlei Wang. "Correlation Degree and Clustering Analysis-Based Alarm Threshold Optimization." Processes 10, no. 2 (January 25, 2022): 224. http://dx.doi.org/10.3390/pr10020224.

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In industrial practice, excessive alarms and high alarm rates are mostly generated from unreasonable settings to variable alarm thresholds, which have become the significant causes of impact on operation stability and plant safety. A correlation degree and clustering analysis-based approach was presented to optimize the variable alarm thresholds in this paper. The correlation degrees of variables are first obtained by analyzing correlation relationships among them. Second, the variables are grouped according to the gray correlation coefficients and clustering analysis, given the weight for fault alarm rate (FAR) in each group. An objective function about the FAR, missed alarm rate (MAR), and the maximum acceptable FAR and MAR is then established with variable weight. Eventually, based on an optimization algorithm, the objective function can be optimized for obtaining the optimal alarm threshold. Cases study of the Tennessee Eastman (TE) industrial simulation process and an actual industrial ethylene production process, in comparison to the initial situation, show that the method can effectively reduce FAR according to correlation degrees among variables in the system, and decrease the number of alarms with reduction rates of 40.5% and 35.3%, respectively.

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26

Fujiwara, Koichi, Manabu Kano, and Shinji Hasebe. "Correlation-based Spectral Clustering for Flexible Soft-Sensor Design." IFAC Proceedings Volumes 43, no. 5 (2010): 703–8. http://dx.doi.org/10.3182/20100705-3-be-2011.00116.

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27

Ordonez, Carlos. "Models for association rules based on clustering and correlation." Intelligent Data Analysis 13, no. 2 (April 17, 2009): 337–58. http://dx.doi.org/10.3233/ida-2009-0369.

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28

Wang, Sheng, Jianfeng Lu, Xingjian Gu, Benjamin A. Weyori, and Jing-yu Yang. "Unsupervised discriminant canonical correlation analysis based on spectral clustering." Neurocomputing 171 (January 2016): 425–33. http://dx.doi.org/10.1016/j.neucom.2015.06.043.

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29

Yoo, YoungJun. "Data-driven fault detection process using correlation based clustering." Computers in Industry 122 (November 2020): 103279. http://dx.doi.org/10.1016/j.compind.2020.103279.

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30

Fukunaga, Takuro. "LP-based pivoting algorithm for higher-order correlation clustering." Journal of Combinatorial Optimization 37, no. 4 (October 22, 2018): 1312–26. http://dx.doi.org/10.1007/s10878-018-0354-y.

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31

Hüsch, Marc, Bruno U. Schyska, and Lueder von Bremen. "CorClustST—Correlation-based clustering of big spatio-temporal datasets." Future Generation Computer Systems 110 (September 2020): 610–19. http://dx.doi.org/10.1016/j.future.2018.04.002.

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32

Liu, Yongxin, Bin Song, Linong Wang, Jiachen Gao, and Rihong Xu. "Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN." Applied Sciences 10, no. 13 (June 27, 2020): 4440. http://dx.doi.org/10.3390/app10134440.

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The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.

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Cen, Yuefeng, Mingxing Luo, Gang Cen, Cheng Zhao, and Zhigang Cheng. "Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering." Future Internet 14, no. 11 (November 14, 2022): 331. http://dx.doi.org/10.3390/fi14110331.

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It is meaningful to analyze the market correlations for stock selection in the field of financial investment. Since it is difficult for existing deep clustering methods to mine the complex and nonlinear features contained in financial time series, in order to deeply mine the features of financial time series and achieve clustering, a new end-to-end deep clustering method for financial time series is proposed. It contains two modules: an autoencoder feature extraction network based on TCN (temporal convolutional neural) networks and a temporal clustering optimization algorithm with a KL (Kullback–Leibler) divergence. The features of financial time series are represented by the causal convolution and the dilated convolution of TCN networks. Then, the pre-training results based on the KL divergence are fine-tuned to make the clustering results discriminative. The experimental results show that the proposed method outperforms existing deep clustering and general clustering algorithms in the CSI 300 and S&P 500 index markets. In addition, the clustering results combined with an inference strategy can be used to select stocks that perform well or poorly, thus guiding actual stock market trades.

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34

Jayaneththi, J. K. D. B. G., and Banage T. G. S. Kumara. "Clustering-Based Approach for Clustering Journals in Computer Science." International Journal of Systems and Service-Oriented Engineering 9, no. 2 (April 2019): 35–51. http://dx.doi.org/10.4018/ijssoe.2019040103.

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In the present scientific world, most of the authors of scientific literature are seeking effective ways to share their research findings with large peer groups. But finding a high-quality journal to publish paper is a huge challenge. Most of the journals present today are predatory and less-quality. The main aim of this study is to help the researchers in identifying the quality level of computer science journals by introducing a data mining model based on six journal quality metrics (Journal Impact Factor, SCImago Journal Rank, Eigenfactor, H-index, Source Normalized Impact per Paper, and Article Influence). Further, another objective is to identify the best metrics to measure the quality of journals out of the six attributes. A sample dataset of 200 journals was used and journals were clustered into five clusters using K-means clustering algorithm. When finding the best quality metrics, Pearson's and Spearman's correlation coefficients were calculated. A more accurate clustering model with an accuracy of 0.9171 was developed considering only suitable attributes.

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35

Aszalós, László. "Decompose Boolean Matrices with Correlation Clustering." Entropy 23, no. 7 (July 2, 2021): 852. http://dx.doi.org/10.3390/e23070852.

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One of the tasks of data science is the decomposition of large matrices in order to understand their structures. A special case of this is when we decompose relations, i.e., logical matrices. In this paper, we present a method based on the similarity of rows and columns, which uses correlation clustering to cluster the rows and columns of the matrix, facilitating the visualization of the relation by rearranging the rows and columns. In this article, we compare our method with Gunther Schmidt’s problems and solutions. Our method produces the original solutions by selecting its parameters from a small set. However, with other parameters, it provides solutions with even lower entropy.

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36

Mai, Derong, Rod E. Turochy, and David H. Timm. "Correlation-Based Clustering of Traffic Data for the Mechanistic–Empirical Pavement Design Guide." Transportation Research Record: Journal of the Transportation Research Board 2339, no. 1 (January 2013): 104–11. http://dx.doi.org/10.3141/2339-12.

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Development of traffic data clusters is crucial for use of the Mechanistic–Empirical Pavement Design Guide (MEPDG) when site-specific traffic data are not available and statewide data are too general. However, a preferred approach to traffic data clustering is not specified in the MEPDG. In current clustering practice, subjective decisions are made about issues such as determination of the number of clusters. This paper presents a new clustering combination method, correlation-based clustering, that considers the effects of traffic inputs on pavement design thicknesses, so that determination of the number of clusters is made objectively. For each traffic input required in the MEPDG, the similarity between two sites is evaluated with Pearson's correlation coefficient. Then, this approach evaluates the sensitivity of pavement design thickness to each traffic input to quantify locations to cut the hierarchical clustering trees, which objectively determines the number of clusters. The MEPDG requires many traffic inputs, including vehicle class distributions, four types of axle load spectra (per vehicle class), monthly and hourly distribution factors, and distributions of axle groups per vehicle. This clustering approach is performed for each traffic input so that a unique set of clusters can be developed for each traffic input. The method has been implemented for 22 direction-specific weigh-in-motion stations in Alabama to identify clusters of sites with similar estimated pavement performance for each traffic input of the MEPDG. This paper illustrates the clustering process for one traffic input (single-axle distribution) and presents clustering results for vehicle class distribution.

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37

Gao, Wenyun, Sheng Dai, Stanley Ebhohimhen Abhadiomhen, Wei He, and Xinghui Yin. "Low Rank Correlation Representation and Clustering." Scientific Programming 2021 (February 16, 2021): 1–12. http://dx.doi.org/10.1155/2021/6639582.

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Correlation learning is a technique utilized to find a common representation in cross-domain and multiview datasets. However, most existing methods are not robust enough to handle noisy data. As such, the common representation matrix learned could be influenced easily by noisy samples inherent in different instances of the data. In this paper, we propose a novel correlation learning method based on a low-rank representation, which learns a common representation between two instances of data in a latent subspace. Specifically, we begin by learning a low-rank representation matrix and an orthogonal rotation matrix to handle the noisy samples in one instance of the data so that a second instance of the data can linearly reconstruct the low-rank representation. Our method then finds a similarity matrix that approximates the common low-rank representation matrix much better such that a rank constraint on the Laplacian matrix would reveal the clustering structure explicitly without any spectral postprocessing. Extensive experimental results on ORL, Yale, Coil-20, Caltech 101-20, and UCI digits datasets demonstrate that our method has superior performance than other state-of-the-art compared methods in six evaluation metrics.

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38

Sowmiya, N., N. Srinivasa Gupta, Elango Natarajan, B. Valarmathi, I. Elamvazuthi, S. Parasuraman, Chun Ang Kit, Lídio Inácio Freitas, and Ezra Morris Abraham Gnanamuthu. "COIN: Correlation Index-Based Similarity Measure for Clustering Categorical Data." Mathematical Problems in Engineering 2022 (September 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/4414784.

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In this paper, a correlation index-based clustering algorithm (COIN) is proposed for clustering the categorical data. The proposed algorithm was tested on nine datasets gathered from the University of California at Irvine (UCI) repository. The experiments were made in two ways, one by specifying the number of clusters and another without specifying the number of clusters. The proposed COIN algorithm is compared with five existing categorical clustering algorithms such as Mean Gain Ratio (MGR), Min–Min-Roughness (MMR), COOLCAT, K-ANMI, and G-ANMI. The result analysis clearly reports that COIN outperforms other algorithms. It produced better accuracies for eight datasets (88.89%) and slightly lower accuracy for one dataset (11%) when compared individually with MMR, K-ANMI, and MGR algorithms. It produced better accuracies for all nine datasets (100%) when it is compared with G-ANMI and COOLCAT algorithms. When COIN was executed without specifying the number of clusters, it outperformed MGR for 88.89% of the test instances and produced lower accuracy for 11% of the test instances.

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39

Shi, Jessica, Laxman Dhulipala, David Eisenstat, Jakub Łăcki, and Vahab Mirrokni. "Scalable community detection via parallel correlation clustering." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2305–13. http://dx.doi.org/10.14778/3476249.3476282.

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Graph clustering and community detection are central problems in modern data mining. The increasing need for analyzing billion-scale data calls for faster and more scalable algorithms for these problems. There are certain trade-offs between the quality and speed of such clustering algorithms. In this paper, we design scalable algorithms that achieve high quality when evaluated based on ground truth. We develop a generalized sequential and shared-memory parallel framework based on the LAMBDACC objective (introduced by Veldt et al.), which encompasses modularity and correlation clustering. Our framework consists of highly-optimized implementations that scale to large data sets of billions of edges and that obtain high-quality clusters compared to ground-truth data, on both unweighted and weighted graphs. Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection. For example, on a 30-core machine with two-way hyper-threading, our implementations achieve orders of magnitude speedups over other correlation clustering baselines, and up to 28.44× speedups over our own sequential baselines while maintaining or improving quality.

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40

Zhang, Bei, Luquan Wang, and Yuanyuan Li. "Precision Marketing Method of E-Commerce Platform Based on Clustering Algorithm." Complexity 2021 (March 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/5538677.

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In user cluster analysis, users with the same or similar behavior characteristics are divided into the same group by iterative update clustering, and the core and larger user groups are detected. In this paper, we present the formulation and data mining of the correlation rules based on the clustering algorithm through the definition and procedure of the algorithm. In addition, based on the idea of the K-mode clustering algorithm, this paper proposes a clustering method combining related rules with multivalued discrete features (MDF). In this paper, we construct a method to calculate the similarity between users using Jaccard distance and combine correlation rules with Jaccard distances to improve the similarity between users. Next, we propose a clustering method suitable for MDF. Finally, the basic K-mode algorithm is improved by the similarity measure method combining the correlation rule with the Jaccard distance and the cluster center update method which is the ARMDKM algorithm proposed in this paper. This method solves the problem that the MDF cannot be effectively processed in the traditional model and demonstrates its theoretical correctness. This experiment verifies the correctness of the new method by clustering purity, entropy, contour, and other indicators.

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41

Kwon, Suhyun, and Man Sik Park. "Time-series Data Clustering Based on the Correlation of Periodogram." Korean Data Analysis Society 22, no. 5 (October 30, 2020): 1751–66. http://dx.doi.org/10.37727/jkdas.2020.22.5.1751.

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42

Priyadharsana, G. Julie. "A Swift Clustering based Algorithm to Explore Different Correlation Measures." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 1317–22. http://dx.doi.org/10.22214/ijraset.2018.3204.

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43

Ha, Hsin-Yu, Fausto C. Fleites, and Shu-Ching Chen. "Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion." International Journal of Multimedia Data Engineering and Management 4, no. 2 (April 2013): 46–64. http://dx.doi.org/10.4018/jmdem.2013040103.

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Анотація:

Nowadays, only processing visual features is not enough for multimedia semantic retrieval due to the complexity of multimedia data, which usually involve a variety of modalities, e.g. graphics, text, speech, video, etc. It becomes crucial to fully utilize the correlation between each feature and the target concept, the feature correlation within modalities, and the feature correlation across modalities. In this paper, the authors propose a Feature Correlation Clustering-based Multi-Modality Fusion Framework (FCC-MMF) for multimedia semantic retrieval. Features from different modalities are combined into one feature set with the same representation via a normalization and discretization process. Within and across modalities, multiple correspondence analysis is utilized to obtain the correlation between feature-value pairs, which are then projected onto the two principal components. K-medoids algorithm, which is a widely used partitioned clustering algorithm, is selected to minimize the Euclidean distance within the resulted clusters and produce high intra-correlated feature-value pair clusters. Majority vote is applied to subsequently decide which cluster each feature belongs to. Once the feature clusters are formed, one classifier is built and trained for each cluster. The correlation and confidence of each classifier are considered while fusing the classification scores, and mean average precision is used to evaluate the final ranked classification scores. Finally, the proposed framework is applied on NUS-wide Lite data set to demonstrate the effectiveness in multimedia semantic retrieval.

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44

Siyou Fotso, Vanel Steve, Engelbert Mephu Nguifo, and Philippe Vaslin. "Frobenius correlation based u-shapelets discovery for time series clustering." Pattern Recognition 103 (July 2020): 107301. http://dx.doi.org/10.1016/j.patcog.2020.107301.

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45

Chormunge, Smita, and Sudarson Jena. "Correlation based feature selection with clustering for high dimensional data." Journal of Electrical Systems and Information Technology 5, no. 3 (December 2018): 542–49. http://dx.doi.org/10.1016/j.jesit.2017.06.004.

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46

Tong, Q., and K. Choi. "Activity correlation-based clustering clock-gating technique for digital filters." International Journal of Electronics 104, no. 7 (March 2, 2017): 1095–106. http://dx.doi.org/10.1080/00207217.2017.1285435.

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47

Nagaraj, R. "Correlation Similarity Measure based Document Clustering with Directed Ridge Regression." Indian Journal of Science and Technology 4, no. 5 (May 20, 2014): 692–97. http://dx.doi.org/10.17485/ijst/2014/v7i5.12.

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48

XU, Zongben, Liwen ZHANG, Shusen YANG, Jian LUO, and Tao WANG. "Maximum average entropy-rate based correlation clustering for big data." SCIENTIA SINICA Informationis 49, no. 12 (December 1, 2019): 1572–85. http://dx.doi.org/10.1360/ssi-2019-0117.

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49

Su, Zhong, Qiang Yang, Hongjiang Zhang, Xiaowei Xu, Yu-Hen Hu, and Shaoping Ma. "Correlation-Based Web Document Clustering for Adaptive Web Interface Design." Knowledge and Information Systems 4, no. 2 (April 2002): 151–67. http://dx.doi.org/10.1007/s101150200002.

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50

Golay, Xavier, Spyros Kollias, Gautier Stoll, Dieter Meier, Anton Valavanis, and Peter Boesiger. "A new correlation-based fuzzy logic clustering algorithm for FMRI." Magnetic Resonance in Medicine 40, no. 2 (August 1998): 249–60. http://dx.doi.org/10.1002/mrm.1910400211.

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