Affinity Propagation (AP) clustering continues
to be proven to work in many of clustering problems. Even
so, almost all of the applications handle static data. The
particular affinity propagation based clustering algorithm
will be individually placed on each and every object
Specific cluster. Utilizing the subsequent clustering
technique . we have object specific Exemplars along with
an increased precision with the data connected with each
and every exemplar. We carry out recognition having a the
vast majority voting strategy that may be weighted simply
by nearest neighbor similarity. This particular paper views
the best way to utilize AP in incremental clustering
problems. Firstly, we mention the problems within
Incremental Affinity Propagation (IAP) clustering, then
propose two techniques to solve them. Correspondingly, two
IAP clustering algorithms are usually proposed. They may
be IAP clustering according to K- Medoids (IAPKM) as well
as IAP clustering depending on Nearest Neighbor
Assignment (IAPNA). Five popular labeled data sets, realworld
time series as well as a video are employed test the
performance associated with IAPKM and IAPNA. Standard
AP clustering is usually implemented to produce benchmark
performance. Experimental results show that IAPKM and
IAPNA is capable of doing comparable clustering
performance together with standard AP clustering on each
of the data sets. Meanwhile, the time cost is actually
dramatically reduced within IAPKM and IAPNA. The two
effectiveness and also the efficiency make IAPKM and
IAPNA capable of being well utilized in incremental
clustering tasks.
Published In : IJCAT Journal Volume 2, Issue 10
Date of Publication : October 2015
Pages : 376 - 380
Figures :02
Tables : 01
Publication Link :Improving the Efficiency in Clustering using
K-Medoids & Nearest Neighbor Assignment
Affinity Propagation
G. Chandrasekhar Reddy : M.Tech 2nd year, Department of CSE, JNTUA, SEAT
Tirupati, AP, India
M. Purushottam Reddy : Associate Professor, Department of CSE, JNTUA, SEAT
Tirupati, AP, India
Cluster
Affinity Propagation
IAP
IAPNA
IAPKM
In this paper, we consider how to apply AP in incremental
clustering task. We firstly point out the difficulty in IAP
clustering, and then propose two strategies to solve it.
Correspondingly, two IAP clustering algorithms, IAPKM
and IAPNA, are proposed. Five popular labeled data sets
and real world time series are used to evaluate the
performance of IAPKM and IAPNA. Experimental results
validate the effectiveness of IAPKM and IAPNA. The
proposition of IAPKM is inspired by combining KMedoids
and AP clustering, where AP clustering is good
at finding an initial exemplar set and K-Medoids is good
at modifying the current clustering result according to
new arriving objects. Experimental results show the
correctness of this idea.
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