Details

Autor(en) / Beteiligte

• Sharma, Pratibha
• Bohra, Brahmdutt
• Yadav, Surendra

Titel

Comparative analysis of web-mining approaches for efficient mining of server log formats

Ist Teil von

• 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2016, p.180-185

Ort / Verlag

IEEE

Erscheinungsjahr

2016

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• In this research, we will talk about the characterization of web information and its order and taking about the records (logs) maintained by the server. Server log documents are fundamentally the ASCII content records which contain the log record of users. The research work is a comparative analysis between web based log formats pre-fetching using two main techniques, i.e. Apriori and FP Growth so that user's navigational behavior can be extracted easily and efficiently. To filter spam with conventional strategies as dark white records (url, IP addresses, mailing information) is practically unimaginable. Use of content mining strategies to a web logs can raise proficiency of a filtration of spam.