Early Defect Detection Using Clustering Algorithms

Botnet Detection using Clustering Algorithms

In this paper, some clustering techniques are analyzed to compare their ability to detect botnet traffic by selecting features that distinguish connections belonging to or not belonging to a botnet. By considering the history of network’s connections, some clustering algorithms are used to derive a set of rules to decide which should be considered as a botnet. Our main contribution is to evalua...

Clustering of Defect Reports Using Graph Partitioning Algorithms

We present in this paper several solutions to the challenging task of clustering software defect reports. Clustering defect reports can be very useful for prioritizing the testing effort and to better understand the nature of software defects. Despite some challenges with the language used and semi-structured nature of defect reports, our experiments on data collected from the open source proje...

Detection of feature freezes using clustering algorithms

Data clustering using community detection algorithms

One of the most important problems in science is that of inferring knowledge from data. The most challenging issue is the unsupervised classification of patterns (observations, measurements, or feature vectors) into groups (clusters) according to their similarity. The quantification of similarity is usually performed in terms of distances or correlations between pairs. The resulting similarity ...

Advanced Defect Detection Algorithm Using Clustering in Ultrasonic NDE

A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the ima...