Generation and Analysis of Tree Structures Based on Association Rules and Hierarchical Clustering

2010 ◽  
Author(s):  
Mihaela Vranic ◽  
Damir Pintar ◽  
Zoran Skocir
Keyword(s):  
Hierarchical Clustering ◽  
Association Rules ◽  
Tree Structures

Tree Structures for Mining Association Rules

2004 ◽  
Vol 8 (1) ◽  
pp. 25-51 ◽  
Author(s):  
Frans Coenen ◽  
Graham Goulbourne ◽  
Paul Leng
Keyword(s):  
Association Rules ◽  
Tree Structures ◽  
Mining Association Rules

scholarly journals Association rules for solar flare attributes through hierarchical clustering

2019 ◽  
Author(s):  
Jéssica de Farias Pereira ◽  
Ana Estela Antunes da Silva
Keyword(s):  
Solar Flare ◽  
Hierarchical Clustering ◽  
Association Rules ◽  
Solar Flares ◽  
Categorical Data ◽  
Binary Data ◽  
Data Set ◽  
The Creation ◽  
Association Algorithm

This project aims to explore a set of association rules through hierarchical clustering as a way to preprocess the rules. The goal is to have a limited set of association rules based on the creation of groups which would indicate promising rules. The data used in the clustering and the construction of the rules are parameters of solar flares. For the construction of a proper data set of solar flares, the following actions were done i) choose a set of data; ii) preprocess the data in order to transform categorical data into binary data; iii) create groups by hierarchical clustering; iv) select groups to create association rules; v) apply the rules using an association algorithm; vi) validate the association rules.

scholarly journals Hierarchical Clustering of DNA K-Mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities

2018 ◽  
Author(s):  
Wolfgang Kaisers ◽  
Holger Schwender ◽  
Heiner Schaal
Keyword(s):  
Hierarchical Clustering ◽  
Sequential Analysis ◽  
Quality Criterion ◽  
Cell Types ◽  
R Package ◽  
Dermal Fibroblasts ◽  
Jurkat Cells ◽  
Batch Effects ◽  
Tree Structures ◽  
Rnaseq Data

We apply hierarchical clustering (HC) of DNA k-mer counts on multiple Fastq files. The tree structures produced by HC may reflect experimental groups and thereby indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. Hence, HC of DNA k-mer counts may serve as an unspecific diagnostic device. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. The approach is validated by analysis of Fastq file batches containing RNAseq data. Analysis of three Fastq batches downloaded from ArrayExpress indicated experimental effects. Analysis of RNAseq data from two cell types (dermal fibroblasts and Jurkat cells) sequenced in our facility indicate presence of batch effects. The observed batch effects were also present in reads mapped to the human genome and also in reads filtered for high quality (Phred > 30). We propose, that hierarchical clustering of DNA k-mer counts provides an unspecific diagnostic tool and a quality criterion and for RNAseq experiments.

scholarly journals Hierarchical Clustering of DNA k-mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities

2018 ◽  
Vol 19 (11) ◽  
pp. 3687
Author(s):  
Wolfgang Kaisers  ◽  
Holger Schwender ◽  
Heiner Schaal 
Keyword(s):  
Hierarchical Clustering ◽  
Sequential Analysis ◽  
Cell Types ◽  
R Package ◽  
Dermal Fibroblasts ◽  
Jurkat Cells ◽  
Batch Effects ◽  
Tree Structures ◽  
Fastq File ◽  
Rnaseq Data

We apply hierarchical clustering (HC) of DNA k-mer counts on multiple Fastq files. The tree structures produced by HC may reflect experimental groups and thereby indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. Hence, HC of DNA k-mer counts may serve as a diagnostic device. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. The approach is validated by analysis of Fastq file batches containing RNAseq data. Analysis of three Fastq batches downloaded from ArrayExpress indicated experimental effects. Analysis of RNAseq data from two cell types (dermal fibroblasts and Jurkat cells) sequenced in our facility indicate presence of batch effects. The observed batch effects were also present in reads mapped to the human genome and also in reads filtered for high quality (Phred > 30). We propose, that hierarchical clustering of DNA k-mer counts provides an unspecific diagnostic tool for RNAseq experiments. Further exploration is required once samples are identified as outliers in HC derived trees.

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