Abstract Machine learning techniques have been widely used in intrusion detection for many
years. However, these techniques are still suffer from lack of labeled dataset, heavy …

Density peaks clustering (DPC) algorithm is a new algorithm based on density clustering
analysis, which can quickly obtain the cluster centers by drawing the decision diagram by …

A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse
nearest neighbor counts as an estimate of observation density. Clustering is performed …

The huge amount of healthcare data, coupled with the need for data analysis tools has
made data mining interesting research areas. Data mining tools and techniques help to …

Due to simplicity, K-means has become a widely used clustering method. However, its
clustering result is seriously affected by the initial centers and the allocation strategy makes …

Community detection is one of the hot topics in the complex networks. It aims to find
subgraphs that are internally dense but externally sparsely connected. In this paper, a new …

Recently, deep clustering, which is able to perform feature learning that favors clustering
tasks via deep neural networks, has achieved remarkable performance in image clustering …

With an exponential increase in the usage of different types of services and applications in
cloud computing environment, the identification of malicious behavior of different nodes …

The synergy between data security and high intensive computing has envisioned the way to
robust anomaly detection schemes which in turn necessitates the need for efficient data …

This paper proposes a novel methodology for delineating urban areas based on a machine
learning algorithm that groups buildings within portions of space of sufficient density. To do …