Adaptive neural network clustering of web users

Santosh K. Rangarajan; Vir V. Phoha; Kiran S. Balagani; Rastko R. Selmic; S. S. Iyengar

doi:10.1109/MC.2004.1297299

Adaptive neural network clustering of web users

Santosh K. Rangarajan, Vir V. Phoha, Kiran S. Balagani, Rastko R. Selmic, S. S. Iyengar

Research output: Contribution to Magazine/Trade Publication › Article

49 Scopus citations

Abstract

A clustering algorithm that groups users according to their Web access patterns was developed and its performance compared with the traditional k-means clustering algorithm. Results show that the new algorithm which is based on ART1 performs better in terms of intracluster distances. The technique was also applied in a prefetching scheme that predicts future user requests. Its prediction accuracy was as high as 97.78 percent.

Original language	English (US)
Pages	34-40
Number of pages	7
Volume	37
No	4
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2004.1297299
State	Published - Apr 2004
Externally published	Yes

ASJC Scopus subject areas

General Computer Science

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10.1109/MC.2004.1297299

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title = "Adaptive neural network clustering of web users",

abstract = "A clustering algorithm that groups users according to their Web access patterns was developed and its performance compared with the traditional k-means clustering algorithm. Results show that the new algorithm which is based on ART1 performs better in terms of intracluster distances. The technique was also applied in a prefetching scheme that predicts future user requests. Its prediction accuracy was as high as 97.78 percent.",

author = "Rangarajan, {Santosh K.} and Phoha, {Vir V.} and Balagani, {Kiran S.} and Selmic, {Rastko R.} and Iyengar, {S. S.}",

year = "2004",

month = apr,

doi = "10.1109/MC.2004.1297299",

language = "English (US)",

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AU - Rangarajan, Santosh K.

AU - Phoha, Vir V.

AU - Balagani, Kiran S.

AU - Selmic, Rastko R.

AU - Iyengar, S. S.

PY - 2004/4

Y1 - 2004/4

N2 - A clustering algorithm that groups users according to their Web access patterns was developed and its performance compared with the traditional k-means clustering algorithm. Results show that the new algorithm which is based on ART1 performs better in terms of intracluster distances. The technique was also applied in a prefetching scheme that predicts future user requests. Its prediction accuracy was as high as 97.78 percent.

AB - A clustering algorithm that groups users according to their Web access patterns was developed and its performance compared with the traditional k-means clustering algorithm. Results show that the new algorithm which is based on ART1 performs better in terms of intracluster distances. The technique was also applied in a prefetching scheme that predicts future user requests. Its prediction accuracy was as high as 97.78 percent.

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Adaptive neural network clustering of web users

Abstract

ASJC Scopus subject areas

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