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Refinement and evaluation of web session cluster quality

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Abstract

Refinement of web session clusters is an open research area these days. The basic reason for proposing the refinement algorithm is quite obvious because in any clustering algorithm the obtained clusters shall have some data items that are inappropriately clustered, hence never giving us 100 % quality. This inappropriateness can be improved through refinement and hence enhance the quality of clusters. In the proposed work, initial clusters are formed using K-Means clustering algorithm which suffers from local minima. The refinement on clusters is performed on the basis of access and time features Modified Knockout Refinement Algorithm (MKRA) which is a distance based dissimilarity measure. Refinement is also performed using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), a combination of GA and PSO and a combination of MKRA, GA and PSO. The issue of local minima is overcome by a combination of GA and PSO. GA and PSO both find a true global optimal solution; GA suffers due to a costly fitness function and expensive computational cost which is resolved by PSO implemented in a linear fashion as it has better computational efficiency. Combination of GA and PSO help to overcome the problem of local minima. Results are evaluated on five synthetic datasets and three real datasets. Further it is shown experimentally that effectiveness of combining MKRA with evolutionary techniques produces well separated and cohesive clusters with improved quality. After getting refined clusters the same can be used to provide recommendations to the target user as an application of web usage clusters. Results show that the accuracy of recommender systems using refined clusters is better than the recommender system implemented using original clusters.

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Dixit, V.S., Bhatia, S.K. Refinement and evaluation of web session cluster quality. Int J Syst Assur Eng Manag 6, 373–389 (2015). https://doi.org/10.1007/s13198-014-0266-x

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