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A unified view of density-based methods for semi-supervised clustering and classification

Jadson Castro Gertrudes, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello
2019 Data mining and knowledge discovery  
We then build upon this view and bridge the areas of semi-supervised clustering and classification under a common umbrella of density-based techniques.  ...  In this paper, we first introduce a unified view of density-based clustering algorithms.  ...  In Sect. 3 we present our unified view of density-based clustering algorithms that bridges between the areas of semi-supervised clustering and classification.  ... 
doi:10.1007/s10618-019-00651-1 pmid:32831623 pmcid:PMC7410108 fatcat:z6gl6cnj3zhb3fupcu3grnhk7a

Correction to: A unified view of density-based methods for semi-supervised clustering and classification

Jadson Castro Gertrudes, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello
2020 Data mining and knowledge discovery  
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ...  School of Mathematical and Physical Sciences, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia  ... 
doi:10.1007/s10618-020-00707-7 fatcat:3rgnr5rnonhdlobfkkovykrjz4

A Review article on Semi- Supervised Clustering Framework for High Dimensional Data

M. Pavithra, R. M. S. Parvathi
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
A brief description of some other semi-supervised clustering algorithms is also provided. Cluster formation has three types as supervised clustering, unsupervised clustering and semi supervised.  ...  Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics.  ...  For distance-based methods, Cohn et al. IV. SEMI-SUPERVISED CLUSTERING METHODS We will now briefly outline several semi-supervised clustering methods.  ... 
doi:10.32628/cseit195410 fatcat:xl37f2eb6bagjfwdscc7fwi2p4

An Artificial Life Approach for Semi-supervised Learning [chapter]

Lutz Herrmann, Alfred Ultsch
2008 Studies in Classification, Data Analysis, and Knowledge Organization  
An approach for the integration of supervising information into unsupervised clustering is presented (semi supervised learning).  ...  Supervising information can be easily incorporated in such a system through the implementation of special movement strategies. These strategies realize given constraints or cluster informations.  ...  In contrast to semi-supervised classification, semi-supervised clustering methods are not limited to the class labels given in the preclassified input samples.  ... 
doi:10.1007/978-3-540-78246-9_17 fatcat:ahjtfx6edvffbicawjhhu73l44

Based on Similarity Metric Learning for Semi-Supervised Clustering

2014 Sensors & Transducers  
This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework.  ...  Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning.  ...  Acknowledgements This work is supported by the science foundation of Guangdong province under grant (No. S2013010013307).  ... 
doaj:cd5b300c2fb142f3ac3a36f91915e698 fatcat:5rz2qogkejez5igo42frf4dkrq

Applicability of semi-supervised learning assumptions for gene ontology terms prediction

Jorge Alberto Jaramillo-Garzón, César Germán Castellanos-Domínguez, Alexandre Perera-Lluna
2016 Revista Facultad de Ingeniería  
Gene Ontology (GO) is one of the most important resources in bioinformatics, aiming to provide a unified framework for the biological annotation of genes and proteins across all species.  ...  The results show that semi-supervised approaches significantly outperform the traditional supervised methods and that the highest performances are reached when applying the cluster assumption.  ...  According to each assumption, there are three main families of semi-supervised methods: generative methods (cluster assumption), density-based methods (low density separation), and graph-based methods  ... 
doi:10.17533/udea.redin.n79a03 fatcat:jbsu5ot3zfbpvfih7t6z6xiiou

LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification

Jawad Khan, Young-Koo Lee
2019 Applied Sciences  
In the absence of enough labeled data, the alternative usage of sentiment lexicons and semi-supervised learning approaches for sentiment classification have substantially attracted the attention of the  ...  (b) training classification models based on a high-quality training dataset generated by using k-mean clustering, active learning, self-learning, and co-training algorithms.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9245562 fatcat:adzlvshbmbfklew457auwrh7ue

Semi-supervised Classification Based Mixed Sampling for Imbalanced Data

Jianhua Zhao, Ning Liu
2019 Open Physics  
In order to improve the classification performance of this kind of problem, this paper proposes a semi-supervised learning algorithm based on mixed sampling for imbalanced data classification (S2MAID),  ...  Firstly, a kind of under sampling algorithm UD-density is provided to select samples with high information content from majority class set for semi-supervised learning.  ...  Semi-supervised learning is divided into semi-supervised clustering and classification.  ... 
doi:10.1515/phys-2019-0103 fatcat:cb56y7t355bpvj5mlpgtbkaejm

Semi-supervised Learning [chapter]

Xiaojin Zhu
2017 Encyclopedia of Machine Learning and Data Mining  
Semi-supervised Clustering Also known as clustering with side information, this is the cousin of semi-supervised classification.  ...  Q: What is semi-supervised learning? A: In this survey we focus on semi-supervised classification. It is a special form of classification.  ... 
doi:10.1007/978-1-4899-7687-1_749 fatcat:a3pujecbsff5nlahnn36cmqdgi

Semi-supervised Collaborative Clustering with Partial Background Knowledge

Germain Forestier, Cédric Wemmert, Pierre Gançarski
2008 2008 IEEE International Conference on Data Mining Workshops  
In this paper we present a new algorithm for semisupervised clustering. We assume to have a small set of labeled samples and we use it in a clustering algorithm to discover relevant patterns.  ...  We study how our algorithm works against two other semi-supervised algorithms when the data are multimodal.  ...  SEMI-SUPERVISED COLLABORATIVE CLUSTERING The proposed approach is divided in two main steps: a collaborative clustering of the data based on an existing collaborative clustering method [11] , and a cluster  ... 
doi:10.1109/icdmw.2008.116 dblp:conf/icdm/ForestierWG08 fatcat:tjq5rxvvkvbp7lu4app4rn6kxe

Adaptive Regularization for Transductive Support Vector Machine

Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael R. Lyu, Zhirong Yang
2009 Neural Information Processing Systems  
Furthermore, we introduce a method of adaptive regularization that is data dependant and is based on the smoothness assumption.  ...  In this framework, SVM and TSVM can be regarded as a learning machine without regularization and one with full regularization from the unlabeled data, respectively.  ...  Acknowledgement The work was supported by the National Science Foundation (IIS-0643494), National Institute of Health (1R01GM079688-01), Research Grants Council of Hong Kong (CUHK4158/08E and CUHK4128/  ... 
dblp:conf/nips/XuJZKLY09 fatcat:ypmehgxhszhpdmju6mekzl7fbu

A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm [article]

Sungwon Han, Yizhan Xu, Sungwon Park, Meeyoung Cha, Cheng-Te Li
2020 arXiv   pre-print
We discuss the practical implications of this method in assisting semi-supervised tasks.  ...  Super-AND outperforms all existing approaches and achieves an accuracy of 89.2% on the image classification task for CIFAR-10.  ...  Application to semi-supervised learning As a practical application, we demonstrate the potential for Super-AND to be used as a pre-training step for well-known semi-supervised learning tasks.  ... 
arXiv:2002.12158v1 fatcat:jvdkn36nyvhxrczibke7ioyn3m

Infinite Mixture Prototypes for Few-Shot Learning [article]

Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum
2019 arXiv   pre-print
In clustering labeled and unlabeled data by the same clustering rule, infinite mixture prototypes achieves state-of-the-art semi-supervised accuracy.  ...  Our infinite mixture prototypes represent each class by a set of clusters, unlike existing prototypical methods that represent each class by a single cluster.  ...  Acknowledgements We gratefully acknowledge support from DARPA grant 6938423 and KA is supported by NSERC. We thank Trevor Darrell and Ghassen Jerfel for advice and helpful discussions.  ... 
arXiv:1902.04552v1 fatcat:mmillrfyqjduxb5vutjwcmiwrm

2020 Index IEEE Transactions on Knowledge and Data Engineering Vol. 32

2021 IEEE Transactions on Knowledge and Data Engineering  
., +, TKDE Nov. 2020 2088-2100 A Semi-Supervised Approach to Message Stance Classification.  ...  ., +, TKDE April 2020 700-713 Multiview Semi-Supervised Learning Model for Image Classification.  ... 
doi:10.1109/tkde.2020.3038549 fatcat:75f5fmdrpjcwrasjylewyivtmu

Clustering-Based Stratified Seed Sampling for Semi-Supervised Relation Classification

Longhua Qian, Guodong Zhou
2010 Conference on Empirical Methods in Natural Language Processing  
Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning.  ...  We systematically evaluate our stratified bootstrapping approach in the semantic relation classification subtask of the ACE RDC (Relation Detection and Classification) task.  ...  Acknowledgments This research is supported by Projects 60873150, 60970056, and 90920004 under the National Natural Science Foundation of China.  ... 
dblp:conf/emnlp/QianZ10 fatcat:qsjsps3wtfga3jgra2q4dqvy5u
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