Effective Document Clustering with Particle Swarm Optimization

Killani, Ramanji; Rao, K. Srinivasa; Satapathy, Suresh Chandra; Pradhan, Gunanidhi; Chandran, Karthik

doi:10.1007/978-3-642-17563-3_73

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…In a recent study [46], some general directives to choose the good combination are propsed as: Swarm size M in [47,48], with a preference for 20 particles, cognitive parameter 1 in [0,1], with a preference for 0.7, social parameter 2~1 .5 with a preference for 1.43. But nevertheless, different parameter values may generate better or worse outcomes depending on the problem, thus the best way to tuning is to make a sensitivity analysis in the context of the problem description.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process

Ergüzel

2015

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Water level control is a crucial step for steam generators (SG) which are widely used to control the temperature of nuclear power plants. The control process is therefore a challenging task to improve the performance of water level control system. The performance assessment is another consideration to underline. In this paper, in order to get better control of water level, the nonlinear process was first expressed in terms of a transfer function (TF), a proportional-integral-derivative (PID) controller was then attached to the model. The parameters of the PID controller was finally optimized using particle swarm optimization (PSO). Simulation results indicate that the proposed approach can make an effective tracking of a given level set or reference trajectory.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

A hybrid PSO-PID approach for trajectory tracking application of a liquid level control process

Ergüzel

2015

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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“…Dash et al (2010) used the hybrid clustering algorithm to find the centroids of a user specified number of clusters, where each cluster groups similar patterns. Killani et al (2010) present a comparative analysis of Kmeans and PSO based clustering performances for text datasets. The result in the work shows the PSO based approaches find better solution compared to K-means due to its ability to evaluate many cluster centroids simultaneously in any given time unlike K-means.…”

Section: Optimization Algorithm In Clusteringmentioning

confidence: 99%

An Ontology Based Model for Document Clustering

Sridevi¹,

Nagaveni²

2011

International Journal of Intelligent Information Technologies

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Clustering is an important topic to find relevant content from a document collection and it also reduces the search space. The current clustering research emphasizes the development of a more efficient clustering method without considering the domain knowledge and user’s need. In recent years the semantics of documents have been utilized in document clustering. The discussed work focuses on the clustering model where ontology approach is applied. The major challenge is to use the background knowledge in the similarity measure. This paper presents an ontology based annotation of documents and clustering system. The semi-automatic document annotation and concept weighting scheme is used to create an ontology based knowledge base. The Particle Swarm Optimization (PSO) clustering algorithm can be applied to obtain the clustering solution. The accuracy of clustering has been computed before and after combining ontology with Vector Space Model (VSM). The proposed ontology based framework gives improved performance and better clustering compared to the traditional vector space model. The result using ontology was significant and promising.

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