Does clustering have a test dataset?
As your question is on clustering: In cluster analysis, there usually is no training or test data split. Because you do cluster analysis when you do not have labels, so you cannot “train”. Training is a concept from machine learning, and train-test splitting is used to avoid overfitting.
Do we need to set training set and testing set for clustering?
The testing error will be large, because testing data points will not overlap with the training data. No, this will usually not be possible. There are very few clusterings that you could use like a classifier. Only with k-means, PAM etc. you could evaluate the “generalization”, but clustering has become much more diverse (and interesting) since.
Which is an objective function in the evaluation of clustering?
Evaluation of clustering. Typical objective functions in clustering formalize the goal of attaining high intra-cluster similarity (documents within a cluster are similar) and low inter-cluster similarity (documents from different clusters are dissimilar). This is an internal criterion for the quality of a clustering.
How is clustering used in exploratory data analysis?
Clustering is one of the most common exploratory data analysis technique used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different.
How does the k-means clustering algorithm work?
It tries to make the intra-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum.