We introduce a novel coreference resolution system that models entities and events jointly.
Our iterative method cautiously constructs clusters of entity and event mentions using linear …

DBSCAN is a well-known density based clustering algorithm capable of discovering
arbitrary shaped clusters and eliminating noise data. However, parallelization of DBSCAN is …

In a relatively short interval for an emerging technology, nanotechnology has made a
significant economic impact in numerous sectors including semiconductor manufacturing …

Problems such as traffic congestion and environmental sustainability are forcing us to review
our long-term plans for transport, whose aim should be to develop and improve safety …

OPTICS is a hierarchical density-based data clustering algorithm that discovers arbitrary-
shaped clusters and eliminates noise using adjustable reachability distance thresholds …

DBSCAN is a widely used is density-based clustering algorithm for particle data well-known
for its ability to isolate arbitrarily-shaped clusters and to filter noise data. The algorithm is …

Mobile communication networks produce massive amounts of data which may be useful in
identifying the location of an emergency situation and the area it affects. We propose a one …

When an event occurs in the real world, numerous news reports describing this event start to
appear on different news sites within a few minutes of the event occurrence. This may result …

Data reduction is perhaps the most critical component in retrieving information from big data
(ie, petascale-sized data) in many data-mining processes. The central issue of these data …

Representative-based clustering algorithms are quite popular due to their relative high
speed and because of their sound theoretical foundation. On the other hand, the clusters …