Determining the quality of the results obtained by clustering techniques is a key issue in
unsupervised machine learning. Many authors have discussed the desirable features of …

Unsupervised learning (clustering) deals with instances, which have not been pre-classified
in any way and so do not have a class attribute associated with them. The scope of applying …

We examine whether the quality of different clustering algorithms can be compared by a
general, scientifically sound procedure which is independent of particular clustering …

Clustering, one of the most important unsupervised learning problems, is the task of dividing
a set of objects into clusters such that objects within the same cluster are similar while …

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised
learning. Previous work in the area has employed one of two approaches: 1) Searchbased …

Clustering (or cluster analysis) aims to organize a collection of data items into clusters, such
that items within a cluster are more “similar” to each other than they are to items in the other …

Cluster analysis remains one of the most challenging yet fundamental tasks in unsupervised
learning. This is due in part to the fact that there are no labels or gold standards by which …

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is
useful in a wide variety of applications, including document processing and modern …

Clustering is an important unsupervised classification technique where data points are
grouped such that points that are similar in some sense belong to the same cluster. Cluster …

In unsupervised learning, the problem of finding the appropriate number of clusters-usually
notated as k-is very challenging. Its importance lies in the fact that k is a vital hyperparameter …