Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) simi...
Measuring the similarity between clusterings is a classic problem with several proposed solutions. In this work we focus on measures based on coassociation of data pairs and perfor...
Recent work on distributional methods for similarity focuses on using the context in which a target word occurs to derive context-sensitive similarity computations. In this paper ...
: Locality Sensitive Hash functions are invaluable tools for approximate near neighbor problems in high dimensional spaces. In this work, we are focused on LSH schemes where the si...
Results of an experimental study of an anomaly detection system based on the paradigm of artificial immune systems (AISs) are presented. Network traffic data are mapped into ant...