# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 3 Discussion
### Functional Purpose  
Before performing clustering on the weblog data, it is important to determine the functional purpose of clustering so that it can improve user experience on the website. From Table 1, the different functional purposes of clustering in web usage mining are listed. The most frequent purposes of clustering are web queries categorization, page recommendation, user group discovery, web personalization, user group categorization, and page categorization. These functional purposes are discussed thoroughly in how they can be applied in the case study of the online roadshow. As the web construct of an online roadshow is relatively new, clustering can help to aid the development of a more effective online roadshow website. The contribution of the clustering purposes towards user experience is presented in Table 6. The general functionalities of page recommendation, web personalization and web queries categorization are similar, but they differ in some specific aspects. Page recommendation suggests the content based on similar user group behaviours, while the web personalization is more individualized. Web personalization suggests the content to the user according to the individual preferences. However, some pre-requisite steps shown in Figure 9 are necessary to achieve three aforementioned functional purposes. Page categorization is a pre-requisite step before performing either page recommendation or web personalization. In page categorization, the web pages are only clustered based on a common theme or topic. After that, page recommendation is performed to suggest the web pages to the users based on topics. User group categorization and user group discovery also have their differences. User group discovery is necessary only when the user groups are not known to the organizations. Otherwise, user group categorization can be directly performed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metrics** & **Number of Applications** & **Clustering Quality** \\ \hline Accuracy & 15 & Accuracy \(\uparrow\) \\ \hline Recall & 9 & Recall \(\uparrow\) \\ \hline Precision & 8 & Precision \(\uparrow\) \\ \hline Error rate/MAE & 6 & Error rate/MAE \(\downarrow\) \\ \hline F-measure & 5 & F-measure \(\uparrow\) \\ \hline Entropy & 1 & Entropy \(\downarrow\) \\ \hline Jaccard index & 1 & Index \(\uparrow\) \\ \hline Purity & 1 & Purity \(\uparrow\) \\ \hline R-measure & 1 & R-measure \(\uparrow\) \\ \hline Rand index & 1 & Rand index \(\uparrow\) \\ \hline V-measure & 1 & V-measure \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics for external criteria. Table 7 presents the usage of the partition-based, hierarchical, density-based, and fuzzy-based clustering algorithms for different functional purposes in web usage mining. If the colour of the box is darker, it indicates a higher number of articles from Table 1 applied the clustering technique for that functional purpose. There are some difficulties that need to be considered by researchers when applying the clustering techniques in web usage mining. Many researchers often compared the