The minimum spanning tree‐(MST‐) based clustering method can identify clusters of
arbitrary shape by removing inconsistent edges. The definition of the inconsistent edges is a …

In recent years, graph‐based data clustering algorithms have become popular as they
perform connectivity‐based rather than centroid‐based partitioning. Methods related to …

Today׳ s real-world databases typically contain millions of items with many thousands of
fields. As a result, traditional distribution-based outlier detection techniques have more and …

Traditional minimum spanning tree-based clustering algorithms only make use of
information about edges contained in the tree to partition a data set. As a result, with limited …

Minimum spanning tree (MST)-based clustering is one of the most important clustering
techniques in the field of data mining. Although traditional MST-based clustering algorithm …

Minimum spanning tree (MST) based clustering algorithms have been employed
successfully to detect clusters of heterogeneous nature. Given a dataset of n random points …

Clustering is an approach to grouping data sets into homogeneous groups based on the
characteristics and patterns. Cluster analysis is becoming an increasingly important …

Due to its important applications in data mining, many techniques have been developed for
clustering. For today's real-world databases which typically have millions of items with many …

Due to its important applications in data mining, many techniques have been developed for
outlier detection. In contrast to k-nearest neighbor-based outlier detection techniques such …

Complex systems require the use of an integrated and best balanced set of components.
The integration and the balanced set are crucial issues, which require some sort of verifiable …