DRRA

Data representation and reduction analysis

Copyright ©VUB - Data representation and reduction analysis course 2017.

Authors:

• Ahmed K. A Abdullah @github/antemooo.
• Rencong Tang @github/rencongtang.

An example to data clustering.

The data will be clustered into 9 clusters through kmeans algorithm.

**The example is written and tested with Python 3.6.4 && Python 3.5.2 **

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To setup the dependencies:

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pip3 install -r requirements.txt

OR

pip install -r requirements.txt

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main.py

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A full example is established.

Needs to use other python files: csv_helper.py, consensus_matrix.py, WordsFrequency.py, cluster.py, clean_tweet.py

A well explained example of how to obtain get the cluster files by using the original data To run the code:

python3 main.py

The script will produce 3 .txt files:

• name+ cluster1.csv to cluster9.csv: contains the tweets which have been clustered into 9 clusters, the name corresponds to .

• kmeans_edge: contains all the kmeans edges information for the further use

• kmeans_nodes: contains all the kmeans nodes information for the further use

• DBSCAN_edge: contains all the DBSCAN edges information for the further use

• DBSCAN_nodes: contains all the DBSCAN nodes information for the further use

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csv_helper.py

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A class contains the basic method needed to get the tweets in the .csv file.

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load_csv:

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• get the information in .csv file tweet by tweet

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load_csv2:

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• get the information in .csv file word by word

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consensus_matrix.py

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A simple script that contains different method to calculate the kmenas consensus matrix

The script itself is well documented and each method has a comment explaining the functionality.

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WordsFrequency.py

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A simple script that contains different method to calculate the the most frequency words in each cluster

The script itself is well documented and each method has a comment explaining the functionality.

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cluster.py

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A simple script that contains different method to generate different files

The script itself is well documented and each method has a comment explaining the functionality.