Technological advancement has enabled us to store and process huge amount of data in
relatively short spans of time. The nature of data is rapidly changing, particularly its …

Data mining is an inevitable task in most of the emerging computing technologies as it
debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the …

High‐dimensional data, ie, data described by a large number of attributes, pose specific
challenges to clustering. The so‐called 'curse of dimensionality', coined originally to …

Clustering is a most powerful un-supervised machine learning techniques for division of
instances into homogenous group, which is called cluster. This Clustering is mainly used for …

There is no doubt that clustering is one of the most studied data mining tasks. Nevertheless,
it remains a challenging problem to solve despite the many proposed clustering …

Clustering is the division of data into groups of similar objects. In clustering, some details are
disregarded in exchange for data simplification. Clustering can be viewed as a data …

Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

Clustering large volumes of high-dimensional data is a challenging task. Many clustering
algorithms have been developed to address either handling datasets with a very large …

Because of the fast technological progress, the amount of information which is stored in
databases is rapidly increasing. In addition, new applications require the storage and …

In high-dimensional data, clusters can exist in subspaces that hide themselves from
traditional clustering methods. A number of algorithms have been proposed to identify such …