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Two Level Clustering of Web Log Files to Enhance the Quality of User Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Gopalakrishnann, T. Sengottuvelan, P. Bharathi, Y. Krishna |
| Copyright Year | 2016 |
| Abstract | Web Usage Mining is the extraction of data from the web log files. Usually web log files have many irrelevant data. Therefore, it requires several steps to enhance and improve the quality of the data, as the final result of web usage mining depends on the quality of the data given as input. In order to overcome this problem, a two level clustering approach is been proposed in this paper. First, data is been collected from the web server. Then, preprocessing is done in four different levels such as, data cleaning, user identification, session identification and path completion in order to remove the irrelevant data from the log files. Then first level clustering is done based on the K – medoids clustering Technique followed by a second level clustering on the web log data. KEYWORDS—web usage mining, session identification, path completion, clustering. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://technicaljournalsonline.com/ijeat/VOL%20VII/IJAET%20VOL%20VII%20ISSUE%20II%20APRIL%20JUNE%202016/201672200.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
