Unsupervised learning is very important in the processing of multimedia content as
clustering or partitioning of data in the absence of class labels is often a requirement. This …

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 …

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

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 …

Unsupervised learning is a kind of machine learning strategy utilized for drawing inferences
from the datasets containing input data without any labeled responses. The purpose of the …

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 …

Semi-supervised clustering uses the limited background knowledge to aid unsupervised
clustering algorithms. Recently, a kernel method for semi-supervised clustering has been …

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 …

Since multimedia information has been dramatically increasing, multimedia data mining has
drawn much more attentions than ever. As one of important mining tasks, clustering provided …

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 …