README.md

Sampling Methods to speed up Clustering Algorithms ⭐

K-Means Clustering 🔥

• KDD Biotrain Dataset

• Worms Dataset

Bisecting K-Means Clustering 🔥

• KDD Biotrain Dataset

• Worms Dataset

K-Center Clustering 🔥

• KDD Biotrain Dataset

• Worms Dataset

K-Medoids Clustering 🔥

• K-medoids clustering is performed on artificial dataset because of its expensive runtime
• Artificial Dataset (dataset visualization)

• All sampling methods

Directory Structure 📁

• The directory structure is as follows -
• There are 4 directories for each clustering algorithm

│   Averaged_Data_collection.xlsx
│   README.md
│
├───bisecting
│       kdd_coresets.py
│       kdd_leverage.py
│       kdd_uniform.py
│       kdd_volume.py
│       worms_coresets.py
│       worms_leverage.py
│       worms_uniform.py
│       worms_volume.py
│
├───images
│   │   artificial.png
│   │   kmedoids.png
│   │
│   ├───kdd
│   │       bisectingcoresets.png
│   │       bisectingvol_lev.png
│   │       kcenterall.png
│   │       kmeanscoresets.png
│   │       kmeansvol_lev.png
│   │
│   └───worms
│           bisectingall.png
│           kcenterall.png
│           kmeansall.png
│
├───kcenter
│       kdd_coresets.py
│       kdd_leverage.py
│       kdd_uniform.py
│       kdd_volume.py
│       worms_coresets.py
│       worms_leverage.py
│       worms_uniform.py
│       worms_volume.py
│
├───kdd
│       bio_train.dat
│       kdd_reduced.pickle
│       kdd_reduced_1k.pickle
│       kdd_reduced_20k.pickle
│       kdd_reduced_30k.pickle
│       kdd_reduced_40k.pickle
│
├───kmeans
│       kdd_coresets.py
│       kdd_leverage.py
│       kdd_uniform.py
│       kdd_volume.py
│       worms_coresets.py
│       worms_leverage.py
│       worms_uniform.py
│       worms_volume.py
│
├───kmedoid
│       artificial_all.py
│
└───worms
        README.txt
        worms_2d.png
        worms_2d.txt
        worms_64d.txt
        worms_reduced.pickle
        worms_reduced_20k.pickle
        worms_reduced_30k.pickle
        worms_reduced_40k.pickle

Results 🔥

• Lightweight coresets outperform all other sampling techniques in each combination ofdatasets and clustering algorithms.

• For the KDD dataset, Leverage sampling performs better than Volume sampling, in eachcombination of KDD dataset and clustering algorithms.

• For the Worms dataset, Volume sampling performs better than Leverage sampling, in eachcombination of Worms dataset and clustering algorithms.

• Although lightweight coresets were designed for kmeans, they show a good performance onthe kcenters algorithm as well, beating rest of the sampling techniques.