Document clustering for knowledge discovery using nature-inspired algorithm
As the internet is overload with information, various knowledge based systems are now equipped with data analytics features that facilitate knowledge discovery.This includes the utilization of optimization algorithms that mimics the behavior of insects or animals.This paper presents an experiment...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/12731/1/2.pdf http://repo.uum.edu.my/12731/ http://www.kmice.cms.net.my/ |
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| Summary: | As the internet is overload with information, various knowledge based systems are now equipped with data analytics features that
facilitate knowledge discovery.This includes
the utilization of optimization algorithms that mimics the behavior of insects or animals.This paper presents an experiment on
document clustering utilizing the Gravitation Firefly algorithm (GFA).The advantage of GFA
is that clustering can be performed without
a pre-defined value of k clusters.GFA determines the center of clusters by identifying documents with high force.Upon
identification of the centers, clusters are
created based on cosine similarity measurement.Experimental results demonstrated
that GFA utilizing a random positioning of
documents outperforms existing clustering algorithm such as Particles Swarm Optimization (PSO) and K-means. |
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