Extracting knowledge from web server logs using web usage mining

Eltahir, Mirghani A.; Dafa-Alla, Anour F.A.

doi:10.1109/icceee.2013.6633973

Cited by 9 publications

(1 citation statement)

References 5 publications

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“…Eltahir, Mirghani, and Anour FA Dafa-Alla [2] have proposedto extract the information from web server logs using prediction model.The major problem which faces any website admin or web application system is data increase per-second that is stored in different formats and types in server log files about users, future needs and maintains structure and apprised of website or web services according to their preceding data. Prediction model aims at discovering useful information or learning from usage data registered in log files, based on primary kinds of data used in the mining process.…”

Section: Literature Reviewmentioning

confidence: 99%

Multifactor Naïve Bayes Classification for the Slow Learner Prediction Over Multiclass Student Dataset

Swati¹,

Kaur²

2016

IJCSA

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The high school students must be observed for their slow learning or quick learning abilities to provide them with the best education practices. Such analysis can be perfectly performed over the student performance data. The high school student data has been obtained from the schools from the various regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300 students obtained from one school in the regions has been undergone the test using the proposed model in this paper. The proposed model is based upon the naïve bayes classification model for the data classification using the multi-factor features obtained from the input dataset. The subject groups have been divided into the two primary groups: difficult and normal. The classification algorithm has been applied individually over data grouped in the various subject groups. Both of the early stage classification events have produced the almost similar results, whereas the results obtained from the classification events over the averaging factors and the floating factors told the different story than the early stage classification. The proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input data. The proposed model can be considered as the effective classification model when evaluated from the results described in the earlier sections.

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Section: Literature Reviewmentioning

confidence: 99%