نتایج جستجو برای: supervised clustering

تعداد نتایج: 137572  

2016
Pracheta Sahoo Asif Ekbal Sriparna Saha Diego Mollá Aliod Kaushik Nandan

Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications. By using the information of a small annotated dataset, semi-supervised clustering can produce clusters that are customized to the application domain. In this paper, we present a semi-supervised clustering technique based on a multi-objective evolutionary algorithm (NSGA-II...

Journal: :International Journal of Research in Engineering and Technology 2014

Journal: :international journal of industrial engineering and productional research- 0
m.h. fazel zarandi department of industrial engineering, amirkabir university of technology, tehran, iran m. zarinbal department of industrial engineering, amirkabir university of technology, tehran, iran

image segmentation is an essential issue in image description and classification. currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. moreover, there are many uncertainties and vagueness in images, which crisp clustering and even type-1 fuzzy clustering could not handle. hence, type-...

2014
Wei QIU

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. The focus of this paper is on Metric Learning, with particular interest in incorporating side information to make it semi-supervised. This study is primarily motivated by an application: face-image clustering. In the paper introduces metric learning and semi-supervised clustering, Similarity metric l...

2010
Pranjal Awasthi Reza Bosagh Zadeh

Despite the ubiquity of clustering as a tool in unsupervised learning, there is not yet a consensus on a formal theory, and the vast majority of work in this direction has focused on unsupervised clustering. We study a recently proposed framework for supervised clustering where there is access to a teacher. We give an improved generic algorithm to cluster any concept class in that model. Our al...

2007
Donghai Guan Andrey Gavrilov Weiwei Yuan Young-Koo Lee Sungyoung Lee

* Professor Sungyoung Lee is the corresponding author. Abstract Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer either poor performance of unsupervised learning or lacking of mechanisms to utilize some prior knowledge about data (semi-supervised learning) for improving clustering result. In an effort to archi...

2004
Sugato Basu

In many machine learning domains, there is a large supply of unlabeled data but limited labeled data, which can be expensive to generate. Consequently, semi-supervised learning, learning from a combination of both labeled and unlabeled data, has become a topic of significant recent interest. Our research focus is on semi-supervised clustering, which uses a small amount of supervised data in the...

2004
Nidal M. Zeidat Christoph F. Eick

This paper centers on the discussion of k-medoid-style clustering algorithms for supervised summary generation. This task requires clustering techniques that identify class-uniform clusters. This paper investigates such a novel clustering technique we term supervised clustering. Our work focuses on the generalization of k-medoid-style clustering algorithms. We investigate two supervised cluster...

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...

Journal: :European Journal of Nuclear Medicine and Molecular Imaging 2021

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