GitHub - tri47/unsupervised_learning: Clustering experiments on a bank marketing and a diseased tree datasets and examine the effects on classification with Neural Network

tri47 / unsupervised_learning Public

Notifications You must be signed in to change notification settings
Fork 1
Star 1

Clustering experiments on a bank marketing and a diseased tree datasets and examine the effects on classification with Neural Network

1 star 1 fork Branches Tags Activity

Star

Notifications

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
README.txt		README.txt
bank_full.csv		bank_full.csv
environment.yml		environment.yml
unsupervised_learning.py		unsupervised_learning.py
wilt_full.csv		wilt_full.csv

Repository files navigation

Steps to run the code:
1. Code link: https://github.com/tri47/unsupervised_learning
2. Set up the environment with the libraries in enviroment.yml file (using conda or pip)
The libraries include:
- python=3.7.2
- matplotlib=3.0.3
- numpy=1.16.2
- pandas=0.24.1
- seaborn=0.10.0
- mlrose==1.3.0
3. Run the program to run the experiments and produce the graphs for 2 problems:
Syntax:
python unsupervised_learning.py --dataset="DATANAME" --task="TASKNAME"

DATANAME options:
"tree": the tree dataset
"bank": the bank dataset
TASKNAME options:
"choose_k": run experiements to find optimal number of clusters.
"cluster": run 2 x clustering algorithms (K-means and EM).
"pca": run PCA on the choosen dataset and generate experiement results.
"ica": run ICA and generate experiement results.
"rp": run random projection and generate experiement results.
"feature": run decision tree feature selection method and generate experiement results.
The options below only works for tree dataset:
"NN": Run neural network on projected data .
"NN_clustering_KM": run neural network with KM clustering as an additional feature.
"NN_clustering_EM": run neural network with EM clustering as an additional feature.
"NN_clustering_KM_without_org": run neural network with KM clustering as the only feature.
"NN_clustering_EM_without_org": run neural network with KM clustering as the only feature.

Examples:
Run PCA For bank marketing problem:
python unsupervised_learning.py --dataset="bank" --task="pca"
Run NN on projected data for diseased tree problem
python unsupervised_learning.py --dataset="tree" --task="NN"

4. The data sets are already included. They were obtained from:

1. http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
2. http://archive.ics.uci.edu/ml/datasets/wilt

Reference:
1. Moro, S, Cortez, P. and Rita, P.. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014.
2. Johnson, B., Tateishi, R., Hoan, N., 2013. A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees. International Journal of Remote Sensing, 34 (20), 6969-6982.