HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHM
R.Jensi, und G. Jiji. Advanced Computational Intelligence: An International Journal (ACII), 2 (2):
11(April 2015)
Zusammenfassung
Data clustering is a technique for clustering set of objects into known number of groups. Several
approaches are widely applied to data clustering so that objects within the clusters are similar and objects
in different clusters are far away from each other. K-Means, is one of the familiar center based clustering
algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers
from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global
optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data
clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The
proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental
results, FPAKM is better than FPA and K-Means.
%0 Journal Article
%1 noauthororeditor
%A R.Jensi,
%A Jiji, G.Wiselin
%D 2015
%J Advanced Computational Intelligence: An International Journal (ACII)
%K Analysis Cluster Flower K-Means Pollination algorithm global intelligence natureinspired optimum swarm
%N 2
%P 11
%T HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHM
%U http://airccse.org/journal/acii/papers/2215acii02.pdf
%V 2
%X Data clustering is a technique for clustering set of objects into known number of groups. Several
approaches are widely applied to data clustering so that objects within the clusters are similar and objects
in different clusters are far away from each other. K-Means, is one of the familiar center based clustering
algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers
from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global
optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data
clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The
proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental
results, FPAKM is better than FPA and K-Means.
@article{noauthororeditor,
abstract = {Data clustering is a technique for clustering set of objects into known number of groups. Several
approaches are widely applied to data clustering so that objects within the clusters are similar and objects
in different clusters are far away from each other. K-Means, is one of the familiar center based clustering
algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers
from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global
optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data
clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The
proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental
results, FPAKM is better than FPA and K-Means.},
added-at = {2017-12-20T04:18:12.000+0100},
author = {R.Jensi and Jiji, G.Wiselin},
biburl = {https://www.bibsonomy.org/bibtex/2920b510e5f313073c81d5cfbeb33a5d7/janakirob},
interhash = {eb62e6043c968e70fd50f30dcff69e4f},
intrahash = {920b510e5f313073c81d5cfbeb33a5d7},
issn = {2454 - 3934},
journal = {Advanced Computational Intelligence: An International Journal (ACII) },
keywords = {Analysis Cluster Flower K-Means Pollination algorithm global intelligence natureinspired optimum swarm},
language = {English},
month = {April},
number = 2,
pages = 11,
timestamp = {2017-12-20T04:18:12.000+0100},
title = {HYBRID DATA CLUSTERING APPROACH USING K-MEANS AND FLOWER POLLINATION ALGORITHM},
url = {http://airccse.org/journal/acii/papers/2215acii02.pdf},
volume = 2,
year = 2015
}