Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

Clustering is a solution for classifying enormous data when there is not any early knowledge
about classes. With emerging new concepts like cloud computing and big data and their vast …

The rapid growth of the Web in the past two decades has made it the largest publicly
accessible data source in the world. Web mining aims to discover useful information or …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

Density based clustering algorithms (DBCLAs) rely on the notion of density to identify
clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover …

Phishing has become an increasing threat in online space, largely driven by the evolving
web, mobile, and social networking technologies. Previous phishing taxonomies have …

Clustering algorithms have emerged as an alternative powerful meta-learning tool to
accurately analyze the massive volume of data generated by modern applications. In …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a
group are more similar to each others than objects in different groups. Most of the clustering …

Data Mining is all about data analysis techniques. It is useful for extracting hidden and
interesting patterns from large datasets. Clustering techniques are important when it comes …