Semi-supervised adaptive-height snipping of the hierarchical clustering tree
نویسندگان
چکیده
منابع مشابه
Vignette for HCsnip: An R Package for semi-supervised adaptive-height snipping of the Hierarchical Clustering tree
This vignette shows the use of HCsnip package for extracting clusters from the Hierarchical Clustering (HC) tree in semi-supervised way. Rather than cutting the HC tree at a fixed highest (as existing methods do), it snips the tree at variable heights to extract hidden clusters. Cluster extraction process uses both the data matrix from which HC tree is derived and the available follow-up inform...
متن کاملHCsnip: An R Package for Semi-supervised Snipping of the Hierarchical Clustering Tree
Hierarchical clustering (HC) is one of the most frequently used methods in computational biology in the analysis of high-dimensional genomics data. Given a data set, HC outputs a binary tree leaves of which are the data points and internal nodes represent clusters of various sizes. Normally, a fixed-height cut on the HC tree is chosen, and each contiguous branch of data points below that height...
متن کاملHierarchical tree snipping: clustering guided by prior knowledge
MOTIVATION Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence. RESULTS To improve the biological sig...
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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...
متن کاملHierarchical Clustering for Semi-Supervised Ground Truth Generation
Supervised learning tasks can require a large collection of labeled data for accurate pattern recognition. For recognition of handwritten characters, manually producing ground truths can be very tedious. In this paper, we propose a semisupervised hierarchical clustering method to reduce the necessary amount of human effort required for labeling a dataset of handwritten characters. The experimen...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2015
ISSN: 1471-2105
DOI: 10.1186/s12859-014-0448-1