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In general, the F-scores are higher for data clusters than for randomized clusters (Supplementary Fig. S6).
F-scores are higher for data clusters than for randomized clusters: for k = 66% at which we make predictions, data F-scores of 23%, 17%and16%6% for edge-HIE, node-KM, and edge-KM are higher than those of 17%, 6%and12%2% for their random counterparts, respectively.
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We solve this problem by interpreting locality sensitive hashing as a means for data clustering.
Kim, N., Park, H., He, N., Lee, H. Y. & Yoon, S. QCanvas: an advanced tool for data clustering and visualization of genomics data.
We prove a unique property of single-link distance, based on which an algorithm is designed for data clustering.
In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy.
One of such methods is based on self organizing maps (SOM), which have been successfully used for data clustering, using a two levels clustering approach.
In this paper, we describe and compare some recent linear and non-linear projection algorithms integrating instance-level constraints ("must-link" and "cannot-link") and applied for data clustering.
It has very promising application prospects for data clustering.
For data clustering, the clustering stability methods on model selection techniques have been investigated in [14].
In [15], Chiu's algorithm has been used for data clustering.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com