Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method
The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Emerald Publishing
2016-01-01
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| Series: | Applied Computing and Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221083271400026X |
| _version_ | 1797706374963003392 |
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| author | D.A. Adeniyi Z. Wei Y. Yongquan |
| author_facet | D.A. Adeniyi Z. Wei Y. Yongquan |
| author_sort | D.A. Adeniyi |
| collection | DOAJ |
| description | The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS) reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN) classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution. |
| first_indexed | 2024-03-12T05:50:17Z |
| format | Article |
| id | doaj.art-3106e0dcd7b948d9b3e3b1736e057551 |
| institution | Directory Open Access Journal |
| issn | 2210-8327 |
| language | English |
| last_indexed | 2024-03-12T05:50:17Z |
| publishDate | 2016-01-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj.art-3106e0dcd7b948d9b3e3b1736e0575512023-09-03T05:09:22ZengEmerald PublishingApplied Computing and Informatics2210-83272016-01-011219010810.1016/j.aci.2014.10.001Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification methodD.A. AdeniyiZ. WeiY. YongquanThe major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS) reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN) classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.http://www.sciencedirect.com/science/article/pii/S221083271400026XAutomatedData miningK-Nearest NeighborOn-lineReal-Time |
| spellingShingle | D.A. Adeniyi Z. Wei Y. Yongquan Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method Applied Computing and Informatics Automated Data mining K-Nearest Neighbor On-line Real-Time |
| title | Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method |
| title_full | Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method |
| title_fullStr | Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method |
| title_full_unstemmed | Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method |
| title_short | Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method |
| title_sort | automated web usage data mining and recommendation system using k nearest neighbor knn classification method |
| topic | Automated Data mining K-Nearest Neighbor On-line Real-Time |
| url | http://www.sciencedirect.com/science/article/pii/S221083271400026X |
| work_keys_str_mv | AT daadeniyi automatedwebusagedataminingandrecommendationsystemusingknearestneighborknnclassificationmethod AT zwei automatedwebusagedataminingandrecommendationsystemusingknearestneighborknnclassificationmethod AT yyongquan automatedwebusagedataminingandrecommendationsystemusingknearestneighborknnclassificationmethod |