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Integrating instance-level and attribute-level knowledge into document clustering

Jinlong Wang, Shunyao Wu, Gang Li, Zhe Wei
2011 Computer Science and Information Systems  
In this paper, we present a document clustering framework incorporating instance-level knowledge in the form of pairwise constraints and attribute-level knowledge in the form of keyphrases.  ...  Firstly, we initialize weights based on metric learning with pairwise constraints, then simultaneously learn two kinds of knowledge by combining the distance-based and the constraint-based approaches,  ...  Based on the semi-supervised method integrating pair-wise constraints and attribute preferences [20] , we present a framework for document clustering analysis.  ... 
doi:10.2298/csis100906003w fatcat:cdknvwmarbbvpl5fch2yrj6y6q

Utilization of gene ontology in semi-supervised clustering

Duong D. Doan, Yunli Wang, Youlian Pan
2011 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)  
A small amount (1-2%) of prior knowledge can improve semi-supervised clustering result substantially and the more specific prior knowledge is generally more efficient in guiding the semi-supervised clustering  ...  Semi-supervised clustering incorporating biological relevance as a prior knowledge has been favoured over the past decade. However, selection of prior knowledge has been a challenge.  ...  [ [2] ] present a comprehensive semi-supervised clustering algorithm, MPCKMeans, that integrates both constraint learning and metric learning in clustering processes.  ... 
doi:10.1109/cibcb.2011.5948467 dblp:conf/cibcb/DoanWP11 fatcat:zyf3ap55vbcshdarkbjsyrmasy

Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations [chapter]

Shrenik Lad, Devi Parikh
2014 Lecture Notes in Computer Science  
We demonstrate the effectiveness of our approach by incorporating the proposed attributebased explanations in three standard semi-supervised clustering algorithms: Constrained K-Means, MPCK-Means, and  ...  Semi-supervised approaches such as distance metric learning and constrained clustering thus leverage user-provided annotations indicating which pairs of images belong to the same cluster (must-link) and  ...  [4] propose an integrated framework that incorporates constrained-clustering and distance metric learning.  ... 
doi:10.1007/978-3-319-10599-4_22 fatcat:sm6a7xht2zewfn7tv3tgll4m4i

Semi-Supervised Clustering With Multiresolution Autoencoders

Dino Ienco, Ruggero G. Pensa
2018 2018 International Joint Conference on Neural Networks (IJCNN)  
Usually, in the semi-supervised clustering setting, the background knowledge is converted to some kind of constraint and, successively, metric learning or constrained clustering are adopted to obtain the  ...  Semi-supervised clustering can be used to drive the algorithmic process with prior knowledge and to enable the discovery of clusters that meet the analyst's expectations.  ...  The authors acknowledge the support of the National Research Agency within the framework of the program "Investissements d'Avenir" for the GEOSUD project (ANR-10-EQPX-20).  ... 
doi:10.1109/ijcnn.2018.8489353 dblp:conf/ijcnn/IencoP18 fatcat:yjqx44c4bngezmyg32zxpid22u

Semi-supervised clustering with metric learning: An adaptive kernel method

Xuesong Yin, Songcan Chen, Enliang Hu, Daoqiang Zhang
2010 Pattern Recognition  
Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints.  ...  Then, we use pairwise constraint-based K-means approach to solve the violation issue of constraints and to cluster the data.  ...  The research reported in this paper has been partially supported by National Science Foundations of China under Grant Nos. 60875030 and 60773061.  ... 
doi:10.1016/j.patcog.2009.11.005 fatcat:vviwynkaszgmnmqkogeiopjvre

Semi-supervised ranking on very large graphs with rich metadata

Bin Gao, Tie-Yan Liu, Wei Wei, Taifeng Wang, Hang Li
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
Specifically, we define a semi-supervised learning framework for ranking of nodes on a very large graph and derive within our proposed framework an efficient algorithm called Semi-Supervised PageRank.  ...  This paper addresses the problem and proposes a general framework as well as an efficient algorithm for graph ranking.  ...  SEMI-SUPERVISED PAGERANK In this section, we propose an efficient algorithm named Semi-Supervised PageRank (SSP) under the general framework.  ... 
doi:10.1145/2020408.2020430 dblp:conf/kdd/GaoLWWL11 fatcat:bpasrgwlavanjfdpnh5b6jdd7q

Deep Multi-view Semi-supervised Clustering with Sample Pairwise Constraints [article]

Rui Chen, Yongqiang Tang, Wensheng Zhang, Wenlong Feng
2022 arXiv   pre-print
multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss.  ...  Then, we innovatively propose to integrate pairwise constraints into the process of multi-view clustering by enforcing the learned multi-view representation of must-link samples (cannot-link samples) to  ...  Acknowledgment The authors are thankful for the financial support by the National Key Research and Development Program of China (2020AAA0109500), the Key-Area Research and Development Program of Guangdong  ... 
arXiv:2206.04949v1 fatcat:s3vmjwaxznh23hhhzh65oskz3e

On defining affinity graph for spectral clustering through ranking on manifolds

Tian Xia, Juan Cao, Yong-dong Zhang, Jin-tao Li
2009 Neurocomputing  
Meanwhile the proposed definition of affinity graph is applicable to both unsupervised and semi-supervised spectral clustering.  ...  Spectral clustering consists of two distinct stages: (a) construct an affinity graph from the dataset and (b) cluster the data points through finding an optimal partition of the affinity graph.  ...  We would like to express our appreciation to the editor and all anonymous reviewers for their insightful comments. We also thank Ping Luo and Liang Wang for their useful suggestions to this paper.  ... 
doi:10.1016/j.neucom.2009.03.012 fatcat:3xp3kleogzhsho4pnrvwqjxmyu

Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering

Norah Ibrahim Fantoukh, Mohamed Maher Ben Ismail, Ouiem Bchir
2020 International Journal of Fuzzy Logic and Intelligent Systems  
Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima.  ...  In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA).  ...  Acknowledgements The authors are grateful for the support they received from the Research Center of the College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.  ... 
doi:10.5391/ijfis.2020.20.2.156 fatcat:wqkp22mlcrc4hon3dnfljdfo3u

Semi-Supervised Multi-Task Regression [chapter]

Yu Zhang, Dit-Yan Yeung
2009 Lecture Notes in Computer Science  
Semi-supervised learning and multi-task learning are two of the approaches that have been proposed to alleviate this problem.  ...  In this paper, we seek to integrate these two approaches for regression applications.  ...  Conclusion In this paper, we have proposed an approach for integrating semi-supervised regression and multi-task regression under a common framework.  ... 
doi:10.1007/978-3-642-04174-7_40 fatcat:hficq3a67zfz5px2rfvkavhvki

SMVC

Stephan Günnemann, Ines Färber, Matthias Rüdiger, Thomas Seidl
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
Independent of this research area, semi-supervised clustering techniques have shown to substantially improve clustering results for single-view clustering by integrating prior knowledge.  ...  We propose a Bayesian framework modeling multiple clusterings of the data by multiple mixture distributions, each responsible for an individual set of relevant dimensions.  ...  X i, BAYESIAN FRAMEWORK In this section, we introduce a Bayesian framework for semi-supervised multi-view clustering.  ... 
doi:10.1145/2623330.2623734 dblp:conf/kdd/GunnemannFRS14 fatcat:ny3xg3m7urc3dcdbgi7dmjct3a

Clustering With Pairwise Relationships: A Generative Approach [article]

Yen-Yun Yu, Shireen Y. Elhabian, Ross T. Whitaker
2018 arXiv   pre-print
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense  ...  Existing algorithms incorporate such user input, heuristically, as either hard constraints or soft penalties, which are separate from any generative or statistical aspect of the clustering model; this  ...  Related Work Semi-supervised clustering methods typically fall into one of two categories [6] : distance-based methods and constraint-based methods.  ... 
arXiv:1805.02285v1 fatcat:u3xi5ghpaneidjo6idgjmtialm

A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep  ...  Semi-supervised Evolutionary Autoencoder [73] An evolutionary autoencoder for dynamic community detection SENet Spectral Embedding Network [86] Spectral embedding network for attributed graph clustering  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

A social learning analytics approach to cognitive apprenticeship

Eman Abu Khousa, Yacine Atif, Mohammad M. Masud
2015 Smart Learning Environments  
Our proposed Fuzzy Pairwise-constraints K-Means (FCKM) algorithm is validated empirically using a two-dimensional synthetic dataset.  ...  The experimental results show improved performance of our clustering approach compared to baseline methods.  ...  For pairwise constrained clustering, we consider a framework that has pairwise must-link and cannot-link constraints (with an associated cost of violating each constraint) between instants in a dataset  ... 
doi:10.1186/s40561-015-0021-z fatcat:262il7klrbenno3lz6za5mdd54

Why Do Attributes Propagate in Graph Convolutional Neural Networks?

Liang Yang, Chuan Wang, Junhua Gu, Xiaochun Cao, Bingxin Niu
2021 AAAI Conference on Artificial Intelligence  
GRL simply constrains the node representation similar with the original attribute, and encourages the connected nodes possess similar representations (pairwise constraint).  ...  However, the perspective of propagation can't provide an intuitive and unified interpretation to their effect on prevent over-smoothing.  ...  Framework Overview Given an attributed graph G = (V, E, X), the following two requirements are natural to obtain the node representation. 1) Unary Constraint.  ... 
dblp:conf/aaai/0002WGCN21 fatcat:ogy7fv5n7nh2fjsb5vwong4pjm
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