cissagatto/jaccard

This code generate partitions for a multilabel dataset using the Jaccard Index similarity measure. We use HCLUST with 6 linkage metrics to generate several partitions. You may build the partition with the highest coefficient. This code also provide an analysis about the partitioning.

Jaccard

This code generate partitions for a multilabel dataset using the Jaccard Index similarity measure. We use HCLUST with 6 linkage metrics to generate several partitions. You may build the partition with the highest coefficient. This code also provide an analysis about the partitioning

Preparing your experiment

Step-1

Confirms if the folder .......

Step-2

Copy this code and place it where you want. The folder configurations is "~/jaccard"

Step-3

A file called datasets-original.csv must be in the root project folder. This file is used to read information about the datasets and they are used in the code. We have 90 multilabel datasets in this .csv file. If you want to use another dataset, please, add the following information about the dataset in the file:

Parameter	Status	Description
Id	mandatory	Integer number to identify the dataset
Name	mandatory	Dataset name (please follow the benchmark)
Domain	optional	Dataset domain
Instances	mandatory	Total number of dataset instances
Attributes	mandatory	Total number of dataset attributes
Labels	mandatory	Total number of labels in the label space
Inputs	mandatory	Total number of dataset input attributes
Cardinality	optional
Density	optional
Labelsets	optional
Single	optional
Max.freq	optional
Mean.IR	optional
Scumble	optional
TCS	optional
AttStart	mandatory	Column number where the attribute space begins*
AttEnd	mandatory	Column number where the attribute space ends
LabelStart	mandatory	Column number where the label space begins
LabelEnd	mandatory	Column number where the label space ends
Distinct	optional
xn	mandatory	Value for Dimension X of the Kohonen map
yn	mandatory	Value for Dimension Y of the Kohonen map
gridn	mandatory	X times Y value. Kohonen's map must be square
max.neigbors	mandatory	The maximum number of neighbors is given by LABELS -1

• Because it is the first column the number is always 1.

STEP 4

You need to have installed all the R packages required to execute this code on your machine. Check out which are needed in the file libraries.R. This code does not provide any type of automatic package installation! You can use the Conda environment that I created to perform this experiment. Below are the links to download the files.

| download txt | download yml | download yaml |

Run

To run, first enter the folder ~/jaccard/R in a terminal and the type: (check this information)

Rscript jaccard.R [number_dataset] [number_cores] [number_folds] [validation] [folder]

Where:

number_dataset is the dataset number in the datasets.csv file

number_cores is the number of cores that you wanto to use in paralel

number_folds is the number of folds you want for cross-validation

validation 0 if you dont want the validation set and 1 if you want

folder temporary folder like SHM or SCRATCH to speed up the process

Folder Structure

Acknowledgment

• This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
• This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPQ) - Process number 200371/2022-3.
• The authors also thank the Brazilian research agencies FAPESP financial support.

Contact

[email protected]

Links

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Thanks