Taking pictures is part of humans life, but there’s a problem when our computer or our devices do not have enough space to store them due to quality. Nowadays professional cameras or even phones take pictures with high resolution. The higher the pixels per inch, the higher the quality hence bigger the file size. For example, in Samsung Galaxy Ultra taking a picture of 12000x9000 takes around 14 Megabytes of disk space. What could we do to save disk space without compromising quality of the image? This is where Principal Component Analysis (PCA) comes in, this technique allows us to make a transition between a high-dimensional dataset into a reduced one (in this case color components of the image), without losing the relationships between features. So PCA means that we will keep the principal features (color components) of the data set (image), that’s why the name is Principal Component Analysis.

Clustering based on different clustering algorithms

The following paper will try to prove how convenient is the use of an Unsupervised Learning method called Association Rules for Market Basket Analysis in a supermarket or any retail or wholesale related store. Association Rules is a method where we can find relationships or dependencies between variables in datasets. Finding these relationships between variables will provide useful information for decision-making in any business.