K-Means is one of the most popular clustering techniques in unsupervised learning. The k-means approach uses a mathematical distance metric to group data points into clusters. By switching between allocating data points to clusters based on the existing centroids and selecting centroids based on the current assignment of data points to clusters, K-Means determines the optimal centroids.

MDS is a dimensionality reduction technique that is used to project high-dimensional data onto a lower-dimensional space while preserving the pairwise distances between the data points as much as possible. MDS is based on the concept of distance and aims to find a projection of the data that minimizes the differences between the distances in the original space and the distances in the lower-dimensional space.

Apriori Algorithm is a foundational method in data mining used for discovering frequent itemsets and generating association rules. Its significance lies in its ability to identify relationships between items in large datasets which is particularly valuable in market basket analysis.