Prediction of user behavior based on clustering techniques
Also available in printed version: ZA4235 K46 2015 raf
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| Format: | Master's thesis |
| Language: | English |
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Universiti Teknologi Malaysia
2025
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| Online Access: | https://utmik.utm.my/handle/123456789/60247 |
| Abstract | Abstract here |
| _version_ | 1854975067344076800 |
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| author | El-Khodary, Rehab Hassan |
| author2 | Salwani Mohd. Daud, supervisor |
| author_facet | Salwani Mohd. Daud, supervisor El-Khodary, Rehab Hassan |
| author_sort | El-Khodary, Rehab Hassan |
| description | Also available in printed version: ZA4235 K46 2015 raf |
| format | Master's thesis |
| id | utm-123456789-60247 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-602472025-08-21T00:05:24Z Prediction of user behavior based on clustering techniques El-Khodary, Rehab Hassan Salwani Mohd. Daud, supervisor Web usage mining Data mining Also available in printed version: ZA4235 K46 2015 raf The use of web technology has increased by a great extent in the recent times. Millions of users spend time surfing the internet to obtain information or for recreational activities leaving many untapped data behind that can be used in several commercial domains like mobile marketing, ecommerce websites, social networking sites and entrepreneur franchises. This creates new possibilities to communicate with the web users based on their historical data and target their interests and satisfaction. The overall aim of this research is to enhance the accuracy of predicting the user’s future behavioral patterns. Web Usage Mining is an important type of Web Mining, which deals with extraction of interesting knowledge from the web log files. There have been rigorous research works that done in this field but basically this research emphasize on the prediction of user behavior to target the users with personalized features. This research also reports comparisons between the web usage mining methods with introducing different applications, which gives the development overview of the research. A system is implemented using the web log files taken from UTM web server, all the data mining processes is applied practically. The pattern discovery process is applied in two different ways, the first one by applying the unsupervised k-means clustering on all users as one data set and the test results is about 79%. While the second one is done by integrating the unsupervised K-means clustering with the supervised Probabilistic Neural Network, this process is applied on every user data set individually. Then, the system is tested and yields a good results that reaches 82%. Finally, the system achieved a good accuracy level comparing with other systems. sof UTM 101 p. Thesis (Sarjana Sains (Kejuruteraan Sistem Komputer))-Universiti Teknologi Malaysia, 2015 2025-03-17T06:35:12Z 2025-03-17T06:35:12Z 2015 Master's thesis https://utmik.utm.my/handle/123456789/60247 valet-20170515-132937 vital:100304 ENG Closed Access UTM Complete Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Web usage mining Data mining El-Khodary, Rehab Hassan Prediction of user behavior based on clustering techniques |
| thesis_level | Master |
| title | Prediction of user behavior based on clustering techniques |
| title_full | Prediction of user behavior based on clustering techniques |
| title_fullStr | Prediction of user behavior based on clustering techniques |
| title_full_unstemmed | Prediction of user behavior based on clustering techniques |
| title_short | Prediction of user behavior based on clustering techniques |
| title_sort | prediction of user behavior based on clustering techniques |
| topic | Web usage mining Data mining |
| url | https://utmik.utm.my/handle/123456789/60247 |
| work_keys_str_mv | AT elkhodaryrehabhassan predictionofuserbehaviorbasedonclusteringtechniques |