Project for CSCI 4971 Large Scale Matrix Computation and Machine Learning exploring spectral clustering algorithms on very large data sets. Specifically, I examine the algorithm presented in "Time and Space Efficient Spectral Clustering via Column Sampling" by Li et al.
Get started by running make data to download the data sets used in this experiment.
You can then run the benchmarking python script, src\benchmark.py:
$ python src\benchmark.py -h
usage: Benchmark spectral clustering algorithms [-h]
[--subset [SUBSET [SUBSET ...]]]
[--iterations ITERATIONS]
[--columns COLUMNS]
[--gamma GAMMA]
d a [a ...]
positional arguments:
d data set to use in benchmarking
a algorithms to run
optional arguments:
-h, --help show this help message and exit
--subset [SUBSET [SUBSET ...]], -s [SUBSET [SUBSET ...]]
use only a subset of classes from the data set
--iterations ITERATIONS, -i ITERATIONS
number of iterations to average over
--columns COLUMNS, -m COLUMNS
number of columns to sample
--gamma GAMMA, -g GAMMA
KASP data reduction ratio
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent