3 research outputs found
A New Clustering Algorithm Based on Near Neighbor Influence
This paper presents Clustering based on Near Neighbor Influence (CNNI), a new
clustering algorithm which is inspired by the idea of near neighbor and the
superposition principle of influence. In order to clearly describe this
algorithm, it introduces some important concepts, such as near neighbor point
set, near neighbor influence, and similarity measure. By simulated experiments
of some artificial data sets and seven real data sets, we observe that this
algorithm can often get good clustering quality when making proper value of
some parameters. At last, it gives some research expectations to popularize
this algorithm.Comment: 21 pages, 9 figures, and 8 table
Synchronization Clustering based on a Linearized Version of Vicsek model
This paper presents a kind of effective synchronization clustering method
based on a linearized version of Vicsek model. This method can be represented
by an Effective Synchronization Clustering algorithm (ESynC), an Improved
version of ESynC algorithm (IESynC), a Shrinking Synchronization Clustering
algorithm based on another linear Vicsek model (SSynC), and an effective
Multi-level Synchronization Clustering algorithm (MSynC). After some analysis
and comparisions, we find that ESynC algorithm based on the Linearized version
of the Vicsek model has better synchronization effect than SynC algorithm based
on an extensive Kuramoto model and a similar synchronization clustering
algorithm based on the original Vicsek model. By simulated experiments of some
artificial data sets, we observe that ESynC algorithm, IESynC algorithm, and
SSynC algorithm can get better synchronization effect although it needs less
iterative times and less time than SynC algorithm. In some simulations, we also
observe that IESynC algorithm and SSynC algorithm can get some improvements in
time cost than ESynC algorithm. At last, it gives some research expectations to
popularize this algorithm.Comment: 37 pages, 9 figures, 3 tabels, 27 conferenc
A Fast Synchronization Clustering Algorithm
This paper presents a Fast Synchronization Clustering algorithm (FSynC),
which is an improved version of SynC algorithm. In order to decrease the time
complexity of the original SynC algorithm, we combine grid cell partitioning
method and Red-Black tree to construct the near neighbor point set of every
point. By simulated experiments of some artificial data sets and several real
data sets, we observe that FSynC algorithm can often get less time than SynC
algorithm for many kinds of data sets. At last, it gives some research
expectations to popularize this algorithm.Comment: 18 pages, 5 figure