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Improved algorithms for Correlation Clustering with local objectives [article]

Sanchit Kalhan, Konstantin Makarychev, Timothy Zhou
<span title="2019-06-21">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2019-02-27">2019</span> <span class="release-stage" >pre-print</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.10829v1">arXiv:1902.10829v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wpwc4yk7drd3bjocu3zua5f4sa">fatcat:wpwc4yk7drd3bjocu3zua5f4sa</a> </span>
We also present an improved (2 + ε) approximation algorithm for this objective.  ...  We also study an alternate cluster-wise local objective introduced by Ahmadi, Khuller and Saha (IPCO '19), which aims to minimize the maximum number of disagreements associated with a cluster.  ...  Puleo and Milenkovic (2016) first studied Correlation Clustering with more local objectives.  ... 
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Sanchit Kalhan, Konstantin Makarychev, Timothy Zhou. "Improved algorithms for Correlation Clustering with local objectives." arXiv (2019)

Improving Prediction Accuracy in Neighborhood-based Collaborative Filtering by Using Local Similarity

Ricardo E. V. de S. Rosa, Felipe A. S. Guimaraes, Rafael S. Mendonca, Vicente F. de Lucena
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;

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For this, we use a clustering method to find groups of similar objects. Then we create a user-based similarity model for each cluster, which we named Cluster-based Local Similarity (CBLS) model.  ...  In this paper, we propose a local similarity method that has the ability to exploit multiple correlation structures between users who express their preferences for objects that are likely to have similar  ...  In practice, selecting users with a higher positive correlation improves the prediction accuracy, whereas including users with lower correlation does not result in significant improvements [36] . s COS  ... 
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Ricardo E. V. de S. Rosa, Felipe A. S. Guimaraes, Rafael S. Mendonca, Vicente F. de Lucena. "Improving Prediction Accuracy in Neighborhood-based Collaborative Filtering by Using Local Similarity." IEEE Access (2020) 1-1

A Non–Convex Optimization Approach to Correlation Clustering

Erik Thiel, Morteza Haghir Chehreghani, Devdatt Dubhashi
<span title="2019-07-17">2019</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;

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We show that the basic approach leads to a simple and natural local search algorithm with guaranteed convergence.  ...  We develop a non-convex optimization approach to correlation clustering using the Frank-Wolfe (FW) framework.  ...  Top plot is a clustering based on majority vote by humans. 1 Algorithm 2 Local search for correlation clustering Initialize v i ∈ {e 1 , ..., e k } randomly for i = 1, ..., n while not converged do Select  ... 
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Erik Thiel, Morteza Haghir Chehreghani, Devdatt Dubhashi. "A Non–Convex Optimization Approach to Correlation Clustering." PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (2019) 5159-5166

Correlation clustering

Francesco Bonchi, David Garcia-Soriano, Edo Liberty
<span title="">2014</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;14</a> </i> &nbsp;

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outliers › outliers are defined as points for which there is no consensus  Improve the robustness of clustering algorithms › different algorithms have different weaknesses. › combining them can  ...  Experiments on synthetic data with planted clustering q = level of noise, |L| = number of labels, K = number of ground truth clusters Extension: multi-chromatic correlation clustering (to appear)  object  ...  This result is a consequence of the existence of local algorithms for correlation clustering. Bonchi, García-Soriano, Kutzkov: Local correlation clustering, arXiv:1312.5105.  ... 
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Citation

Francesco Bonchi, David Garcia-Soriano, Edo Liberty. "Correlation clustering." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14 (2014) 1972

Local Guarantees in Graph Cuts and Clustering [chapter]

Moses Charikar, Neha Gupta, Roy Schwartz
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;

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The classical approach towards Correlation Clustering (and other graph cut problems) is to optimize a global objective.  ...  We depart from this and study local objectives: minimizing the maximum number of disagreements for edges incident on a single node, and the analogous max min agreements objective.  ...  Table 1 : 1 Results for Correlation Clustering with local guarantees.  ... 
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Moses Charikar, Neha Gupta, Roy Schwartz. "Local Guarantees in Graph Cuts and Clustering." Lecture Notes in Computer Science (2017) 136-147

Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data

Liu Yang, Rong Jin, Caroline Pantofaru, Rahul Sukthankar
<span title="">2007</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2007 IEEE Conference on Computer Vision and Pattern Recognition</a> </i> &nbsp;

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We present a direct comparison with a state-of-the art method on the PASCAL 2006 database and show that cluster refinement results in a significant improvement in classification accuracy given a small  ...  While DCR could improve a broad class of information retrieval systems, this paper focuses on object category recognition.  ...  We thank Marcin Marszalek and Cordelia Schmid for making direct comparisons possible by providing us with their interest point detections and descriptors on the PASCAL dataset.  ... 
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Citation

Liu Yang, Rong Jin, Caroline Pantofaru, Rahul Sukthankar. "Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data." 2007 IEEE Conference on Computer Vision and Pattern Recognition (2007)

Multi-Label Learning with Class-Based Features Using Extended Centroid-Based Classification Technique (CCBF)

P.R. Suganya Devi, R. Baskaran, S. Abirami
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cx3f4s3qmfe6bg4qvuy2cxezyu" style="color: black;">Procedia Computer Science</a> </i> &nbsp;

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It includes class-based feature vectors generation and local label correlations exploitation.  ...  Real world applications, such as news feeds categorization deal with multi-label classification problem, where the objects are associated with multiple class labels and each object is represented by a  ...  . , F V 1 (103). (5) This is summarized in the following Algorithm 2. Local label correlation estimation Local label correlations are considered to improve the performance.  ... 
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<span class="ui label basic orange small" title="Open Access">Open Access</span>
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Citation

P.R. Suganya Devi, R. Baskaran, S. Abirami. "Multi-Label Learning with Class-Based Features Using Extended Centroid-Based Classification Technique (CCBF)." Procedia Computer Science (2015) 405-411

Label Distribution Learning by Exploiting Sample Correlations Locally

Xiang Zheng, Xiuyi Jia, Weiwei Li
<span title="2018-04-29">2018</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;

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To encode the influence of local samples, we design a local correlation vector for each instance based on the clustered local samples.  ...  In this paper, we propose a new label distribution learning algorithm by exploiting sample correlations locally (LDL-SCL).  ...  In this paper, the output model and the objective function are improved by taking into account the influence of local sample and an alternating solution is utilized for the optimization.  ... 
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Citation

Xiang Zheng, Xiuyi Jia, Weiwei Li. "Label Distribution Learning by Exploiting Sample Correlations Locally." PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE 32.1 (2018)

Research on the Novel Computer Network Intrusion Detection Model based on Improved Particle Swarm Optimization Algorithm

Juan Fu, Hai Hu, Leping Wang
<span title="2016-12-13">2016</span> <i title="DEStech Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qqom5ourhrgeppukalbg7otv5e" style="color: black;">DEStech Transactions on Social Science Education and Human Science</a> </i> &nbsp;

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In this paper, we conduct research on the novel computer network intrusion detection model based on improved particle swarm optimization algorithm.  ...  Network security protection is a comprehensive project, no matter what measures to take that safety is always relative, so as the network security administrator, should change with the network security  ...  The Improved Particle Swarm Optimization Algorithm.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12783/dtssehs/isetem2016/4386">doi:10.12783/dtssehs/isetem2016/4386</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hgixrzsxtjcorcdug66ftri3v4">fatcat:hgixrzsxtjcorcdug66ftri3v4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719122656/http://dpi-proceedings.com/index.php/dtssehs/article/download/4386/4025" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e3/0c/e30cc7bcb752868428c4b385c94bf0350060c334.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12783/dtssehs/isetem2016/4386"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>
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Juan Fu, Hai Hu, Leping Wang. "Research on the Novel Computer Network Intrusion Detection Model based on Improved Particle Swarm Optimization Algorithm." DEStech Transactions on Social Science Education and Human Science (2016)

Evolutionary algorithms for overlapping correlation clustering

Carlos E. Andrade, Mauricio G.C. Resende, Howard J. Karloff, Flávio K. Miyazawa
<span title="">2014</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fdhfwmjdwjbvxo6zc7cdt5hi7q" style="color: black;">Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO &#39;14</a> </i> &nbsp;

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with objects in more than one cluster.  ...  In Overlapping Correlation Clustering (OCC), a number of objects are assigned to clusters. Two objects in the same cluster have correlated characteristics.  ...  This paper presents Biased Random-Key Genetic Algorithms associated with local search procedures aiming to solve the overlapping correlation clustering problem under an optimization point of view.  ... 
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Carlos E. Andrade, Mauricio G.C. Resende, Howard J. Karloff, Flávio K. Miyazawa. "Evolutionary algorithms for overlapping correlation clustering." Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14 (2014) 405-412

Features Clustering Around Latent Variables for High Dimensional Data

Ez-Zarrad Ghizlane, Sabbar Wafae, Bekkhoucha Abdelkrim, S. Krit
<span title="">2021</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wehsdgmkgvabfewguumql7pepe" style="color: black;">E3S Web of Conferences</a> </i> &nbsp;

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In the present study, we combine two methods of features clustering the clustering of variables around latent variables (CLV) algorithm and the k-means based co-clustering algorithm (kCC).  ...  Clustering of variables is the task of grouping similar variables into different groups.  ...  for local groups.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/e3sconf/202129701070">doi:10.1051/e3sconf/202129701070</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3w43snjcerghdppyuqgnjw5htm">fatcat:3w43snjcerghdppyuqgnjw5htm</a> </span>
<span class="ui label basic orange small" title="Directory of Open Access Journals">DOAJ</span> <span class="ui label basic orange small" title="Szcezepanski OA Journal List">Szczepanski</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211001100018/https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/73/e3sconf_iccsre21_01070.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/66/e5/66e53701cbabe2cd579dbae07d785e1a583aaf3f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/e3sconf/202129701070"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>
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Ez-Zarrad Ghizlane, Sabbar Wafae, Bekkhoucha Abdelkrim, S. Krit. "Features Clustering Around Latent Variables for High Dimensional Data." E3S Web of Conferences (2021) 01070

A novel framework for image forgery localization [article]

Davide Cozzolino and Diego Gragnaniello and Luisa Verdoliva
<span title="2013-11-27">2013</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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According to preliminary experiments on the training set, the proposed framework provides often a very good localization accuracy and sometimes valuable clues for visual scrutiny.  ...  Image forgery localization is a very active and open research field for the difficulty to handle the large variety of manipulations a malicious user can perform by means of more and more sophisticated  ...  the PRNU for the C valid clusters; • associate each test image with one of the clusters; • localize forgeries.  ... 
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Davide Cozzolino and Diego Gragnaniello and Luisa Verdoliva. "A novel framework for image forgery localization." arXiv (2013)

Data-aware 3D partitioning for generic shape retrieval

Ivan Sipiran, Benjamin Bustos, Tobias Schreck
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eu2m4gixpvbj3bgkrmcfu4ezdi" style="color: black;">Computers &amp; graphics</a> </i> &nbsp;

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In this paper, we present a new approach for generic 3D shape retrieval based on a mesh partitioning scheme.  ...  We show that mesh partitions are representative and therefore it helps to improve the effectiveness in retrieval tasks.  ...  Another important aspect is the flexibility of the adaptive clustering algorithm with respect to the obtained number of clusters.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.cag.2013.04.002">doi:10.1016/j.cag.2013.04.002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/luznj763zza3ldyr34n26k7ad4">fatcat:luznj763zza3ldyr34n26k7ad4</a> </span>
<span class="ui label basic orange small" title="Open Access">Open Access</span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811125308/https://users.dcc.uchile.cl/~isipiran/papers/SBS13.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f1/57/f157f07226a826192c0d87aab0daa33ce8fa0565.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.cag.2013.04.002"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>
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Ivan Sipiran, Benjamin Bustos, Tobias Schreck. "Data-aware 3D partitioning for generic shape retrieval." Computers & graphics 37.5 (2013) 460-472

An advanced ilrcpsd technique for bridging the competency and cognitive skills of students in higher education

Joy Christy A.
<span title="2017-12-31">2017</span> <i title="Science Publishing Corporation"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/piy2nrvrjrfcfoz5nmre6zwa4i" style="color: black;">International Journal of Engineering &amp; Technology</a> </i> &nbsp;

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It then computes the distance between initial centroids with the remaining data objects and groups the data objects into the cluster centroids with minimum distance.  ...  The performance of the proposed algorithm is well-compared with the accuracy of the traditional k-means clustering in terms of building meaningful clusters and to prove its real time usefulness.  ...  The clustering algorithm proposed in this chapter also takes the advantage of labeled classifier for correctly clustering the data objects based on the classes so as to improve the accuracy of clustering  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14419/ijet.v7i1.3.8984">doi:10.14419/ijet.v7i1.3.8984</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/txltexx4erfvfcs7hadxrfurse">fatcat:txltexx4erfvfcs7hadxrfurse</a> </span>
<span class="ui label basic orange small" title="Open Access">Open Access</span>
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Joy Christy A.. "An advanced ilrcpsd technique for bridging the competency and cognitive skills of students in higher education." International Journal of Engineering & Technology 7.1.3 (2017) 37

Fuzzy Supervised Clustering Algorithm with the Particle Swarm Optimization

Yuan-horng LIN, Jeng-ming YIH, Shin-hua WU
<span title="2018-08-31">2018</span> <i title="DEStech Publications"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/52spf3of4jbu5da2xrhvubclcy" style="color: black;">DEStech Transactions on Computer Science and Engineering</a> </i> &nbsp;

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In GK-algorithm, fuzzy clustering algorithm with preserved volume was used.  ...  The other important issue is how to approach the global minimum value that can improve the cluster accuracy. The methods to detect the local extreme value were developed by this paper.  ...  FCM-NM Algorithm Not only z-score normalizing for each feature in the objective function in the FCM-CM algorithm, but also replacing the threshold D, where With the determinant value of the crisp correlation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12783/dtcse/ccnt2018/24672">doi:10.12783/dtcse/ccnt2018/24672</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l27ztnw3czbtvlckpzmxebb7e4">fatcat:l27ztnw3czbtvlckpzmxebb7e4</a> </span>
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Yuan-horng LIN, Jeng-ming YIH, Shin-hua WU. "Fuzzy Supervised Clustering Algorithm with the Particle Swarm Optimization." DEStech Transactions on Computer Science and Engineering (2018)

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