# A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
## 3 Discussion
### Functional Purpose
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Functional Purpose** & **Ways to Improve User Experience** & **Clustering Techniques** \\ \hline User group discovery & Discovering the type of users on the website helps to segment the users based on different behavioral patterns. When the user groups of the website are known, developers can improve the website so that it can be catered to different user groups. & _DBSCAN_[44, 45, 49, 67], FCM [54, 55, 56, 57, 59, 69], _k-medus_[31, 62], _CHAMELON_[65], bisecting _k-means_[41], _CIURE_[36], _k-means_[25, 27] \\ \hline User group categorization & Categorizing of users into groups of similar interests helps the developers to improve the recommendation system in the website so that it can suggest web pages to users to sustain their interest. & _FCM_[52], _k-means_[28] \\ \hline Page categorization & Clustering of the web pages groups the web pages into similar content types or themes. Developers can improve the design of the website so that the users can access the pages conveniently based on its content type. & _DBSCAN_[48], _k-medoids_[33], _CHAMELON_[39], _k-means_[23] \\ \hline Web queries categorization & Classifying a web search query to one or more categories based on the topics enables users to easily find their interested topic. Users will feel more comfortable and in control when navigating the website. & _k-means_[24], bisecting _k-means_[66], _FCM_[58] \\ \hline Page recommendation & Providing suggestions of web pages based on similar user group behavior helps to reduce the time spent for the users to search for web pages. & _k-means_[26], _k-medoids_[30, 34], _CHAMELON_[64], _FCM_[53, 70] \\ \hline Web personalization & Customization of the web pages is based on the user’s past browsing activities on the website. A personalized user interface elements based on their preferences allow the users to interact in a familiar environment. & _CURE_[63], _DBSCAN_[47], _k-medoids_[32], _FCM_[68], _CHAMELON_[40], _k-means_[22] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Functional purpose and ways to improve user experience. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Purpose** & \multicolumn{2}{c}{**Partition-Based**} & \multicolumn{2}{c}{**Hierarchical**} & **Density-Based** & **Fuzzy** \\ \cline{2-7}  & _k-Means_ & _k-Medoids_ & _CIURE_ & _CHAMELON_ & **Bisecting _k-Means_** & _DBSCAN_ & _FCM_ \\ \hline User group discovery & **2** & **2** & **1** & **1** & **1** & **4** & **6** \\ \hline User group categorization & 1 & 0 & 0 & 0 & 0 & 0 & 1 \\ \hline Page categorization & 1 & 1 & 0 & 1 & 0 & 1 & 0 \\ \hline Web queries categorization & 1 & 0 & 0 & 0 & 1 & 0 & 1 \\ \hline Page recommendation & 1 & 2 & 0 & 1 & 0 & 0 & 2 \\ \hline Web personalization & 1 & 1 & **1** & **1** & 0 & **2** & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Frequency heatmap of clustering purpose with respect to the clustering techniques. Figure 9: Relationship between the functional purposes. performance of the other clustering techniques with _k-means_ clustering. When identifying unknown user groups, _k-means_ clustering tends to produce empty clusters [31; 41; 42; 55]. This is because of the random initialization of centroids, which causes some centroids to be too close to each other. Compared to _k-means_ clustering, _k-medoids_, _fuzzy c-means_ clustering produce clusters that are more uniform and balanced. In terms of time complexity, _k-means_ executes faster than _fuzzy c-means_ although the clustering quality of _fuzzy c-means_ is higher. _Fuzzy c-means_ takes a longer time to run because it requires more computational time to perform the fuzzy measures. Meanwhile, both _bisecting k-means_ and _k-medoids_ execute faster than _k-means_ as they reduce the clustering computation for data point in each iteration. In [64], _CHAMELEON_ clustering showed a slower execution time than _k-means_, which might cause a delay in a real-time solution.