Once a dissimilarity measure is developed, a clustering algorithm attempts to find
a. clusters of rows where rows within a cluster are dissimilar and rows in different clusters are dissimilar.
b. clusters of rows where rows within a cluster are dissimilar and rows in different clusters are similar.
c. clusters of rows where rows within a cluster are similar and rows in different clusters are dissimilar.
d. clusters of rows where rows within a cluster are similar and rows in different clusters are similar.
The correct answer and explanation is:
The correct answer is c. clusters of rows where rows within a cluster are similar and rows in different clusters are dissimilar.
In clustering algorithms, the primary objective is to group data points (or rows) in such a way that data points within the same group, or cluster, are similar to one another, while data points in different clusters are as dissimilar as possible. This principle relies on a dissimilarity measure, often referred to as a distance metric, which quantifies the degree of similarity or dissimilarity between pairs of data points.
Clustering algorithms, such as k-means or hierarchical clustering, aim to minimize the intra-cluster distance (the distance between data points within the same cluster) and maximize the inter-cluster distance (the distance between data points from different clusters). The goal is to identify natural groupings within the dataset where the points in a group share common characteristics while being distinct from points in other groups.
For example, in a clustering problem where the data points represent customer behaviors, the algorithm might group customers with similar purchasing patterns into one cluster, while customers with very different behaviors are placed into separate clusters. The algorithm ensures that the dissimilarity (or distance) between points in the same cluster is low, and the dissimilarity between points in different clusters is high.
This approach is crucial because it allows the algorithm to reveal underlying patterns or structures in the data. In essence, clustering is about finding the “natural” groupings that make sense based on the measure of similarity, which can be adjusted depending on the problem and the data involved.