نتایج جستجو برای: supervised clustering

تعداد نتایج: 137572  

2012
Xin Huang Hong Cheng Jiong Yang Jeffrey Xu Yu Hongliang Fei Jun Huan

Semi-supervised clustering has recently received a lot of attention in the literature, which aims to improve the clustering performance with limited supervision. Most existing semi-supervised clustering studies assume that the data is represented in a vector space, e.g., text and relational data. When the data objects have complex structures, e.g., proteins and chemical compounds, those semi-su...

2005
Zheng-Yu Niu Dong-Hong Ji Chew Lim Tan

In this paper we investigate an application of feature clustering for word sense disambiguation, and propose a semisupervised feature clustering algorithm. Compared with other feature clustering methods (ex. supervised feature clustering), it can infer the distribution of class labels over (unseen) features unavailable in training data (labeled data) by the use of the distribution of class labe...

2013
YONG YONG YI RAN

In order to solve the difficult questions such as in the presence of the cluster deviation and high dimensional data processing in traditional semi-supervised clustering algorithm, a semi-supervised clustering algorithm based on active learning was proposed, this algorithm can effectively solve the above two problems. Using active learning strategies in algorithm can obtain a large amount of in...

2009
Bojun Yan

As a recent emerging technique, semi-supervised clustering has attracted significant research interest. Compared to traditional clustering algorithms, which only use unlabeled data, semi-supervised clustering employs both unlabeled and supervised data to obtain a partitioning that conforms more closely to the user's preferences. Several recent papers have discussed this problem (Cohn, Caruana, ...

2003
Sugato Basu Mikhail Bilenko Raymond J. Mooney

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has employed one of two approaches: 1) Searchbased methods that utilize supervised data to guide the search for the best clustering, and 2) Similarity-based methods that use supervised data to adapt the underlying similarity metric used by the clustering algorithm. This pape...

Journal: :IEICE Transactions on Information and Systems 2017

2011
L. Sankari

Key factors like similarity, proximity, and good Many researchers have mentioned the significance of perceptual grouping and organization in vision and listed various continuation that guide to visual grouping of image. However, even to the present situation, many of the computational factors of perceptual grouping have remained unanswered. As there are several probable partitions of the domain...

2006
Hariharan Narayanan Mikhail Belkin Partha Niyogi

One of the intuitions underlying many graph-based methods for clustering and semi-supervised learning, is that class or cluster boundaries pass through areas of low probability density. In this paper we provide some formal analysis of that notion for a probability distribution. We introduce a notion of weighted boundary volume, which measures the length of the class/cluster boundary weighted by...

2015
Jamil Ahmed Hasibur Rahman

Semi-supervised clustering aims to improve clustering performance by considering user-provided side information in the form of pairwise constraints. We study the active learning problem of selecting must-link and cannot-link pairwise constraints for semi-supervised clustering. We consider active learning in an iterative framework; each iteration queries are selected based on the current cluster...

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