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 …

The two most widely used and easily implementable algorithm for clustering and
classification-based analysis of data in the unsupervised learning domain are Density …

Abstract Service availability plays a vital role on computer networks, against which
Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year …

Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an
important task for applications such as fraud detection, fault diagnosis and disease …

Image processing is a promising technique for enhancing images or extracting useful
information from them. One commonly used density‐based clustering algorithm is DBSCAN …

In hierarchical clustering, the most important factor is the selection of the linkage method
which is the decision of how the distances between clusters will be calculated. It extremely …

Density peaks clustering (DPC) has been proved to be an effective clustering method and
applied to many scientific fields. It can detect the density peak within each cluster and assign …

Limited studies exist in the literature on demand related travel patterns, the analysis of which
requires a rich database of Origin Destination (OD) matrices with appropriate clustering …

Clustering is the process of partitioning objects of a dataset into some groups according to
similarities and dissimilarities between its objects. DBSCAN is one of the most important …

Image segmentation has been considered as the first step in the image processing. An
efficient segmentation result would make it easier for further analysis of image processing …