نتایج جستجو برای: supervised clustering

تعداد نتایج: 137572  

2006
Yuntao Qian Xiaoxu Du Qi Wang

In many data mining tasks, there is a large supply of unlabeled data but limited labeled data since it is expensive generated. Therefore, a number of semi-supervised clustering algorithms have been proposed, but few of them are specially designed for high dimensional data. High dimensionality is a difficult challenge for clustering analysis due to the inherent sparse distribution, and most of p...

2011
Bruno Magalhães Nogueira Alípio Jorge Solange Oliveira Rezende

Semi-supervised approaches have proven to be effective in clustering tasks. They allow user input, thus improving the quality of the clustering obtained, while maintaining a controllable level of user intervention. Despite being an important class of algorithms, hierarchical clustering has been little explored in semisupervised solutions. In this report, we address the problem of semi-supervise...

2014
Anil Jain Rong Jin Radha Chitta

Clustering is an unsupervised learning problem whose objective is to find a partition of the given data. However, a major challenge in clustering is to define an appropriate objective function in order to to find an optimal partition that is useful to the user. To facilitate data clustering, it has been suggested that the user provide some supplementary information about the data (eg. pairwise ...

2004
Kevin Y. Yip David W. Cheung Michael K. Ng

Recent studies suggest that projected clusters with extremely low dimensionality exist in many real datasets. A number of projected clustering algorithms have been proposed in the past several years, but few can identify clusters with dimensionality lower than 10% of the total number of dimensions, which are commonly found in some real datasets such as gene expression profiles. In this paper we...

2005
Hong Chang Dit-Yan Yeung

Many supervised and unsupervised learning algorithms are very sensitive to the choice of an appropriate distance metric. While classification tasks can make use of class label information for metric learning, such information is generally unavailable in conventional clustering tasks. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised c...

2016
Limin Wang Xing Tao Xuming Han Jialing Han Ying Liu Guangyu Mu Zhengdong Lu

Original scientific paper In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, a semi-supervised affinity propagation clustering algorithm based on density peaks (SAP-DP) was proposed in this paper. The algorithm uses a new algorithm of density peaks (DP) which has the advantage of the manifold cluster...

2012
Artur Abdullin Olfa Nasraoui

We propose a semi-supervised framework to handle diverse data formats or data with mixedtype attributes. Our preliminary results in clustering data with mixed numerical and categorical attributes show that the proposed semi-supervised framework gives better clustering results in the categorical domain. Thus the seeds obtained from clustering the numerical domain give an additional knowledge to ...

Journal: :Neural computation 2000
Alejandro Sierra Fernando J. Corbacho

In some branches of science, such as molecular biology, classes may be defined but not completely trusted. Sometimes posterior analysis proves them to be partially incorrect. Despite its relevance, this phenomenon has not received much attention within the neural computation community. We define reclassification as the task of redefining some given classes by maximum likelihood learning in a mo...

Journal: :Wiley interdisciplinary reviews. Computational statistics 2013
Eric Bair

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. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, informat...

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