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When taking the website live with this snippet, the mouse movements of every visitor to the website are gathered into a packet of data points and sent to a server in JSON format, where the data is saved in a database. The data points that are collected include: clicks, click targets, x,y coordinates of mouse, width and height of the browser and timestamp. This URL would need to be included in the tracker. Because the data packet is in JSON format, saving to a database depends on the type of database.

For example, a document database such as MongoDB means the data can be saved directly to the database. A relational database such as SQL, however, would need some formatting of the data packet into a tabular format to make it easier to be inputted into the database. Conversion of the data packet into tabular format can be done by applying the packetToDF function in the following python script and entering each row into the database see saveToRelational. The below python script gathers all JSON packets in the example file and converts them to one big data frame with the columns of x, y, width, height, isClick etc.

This preprocessing of the data gets us ready to perform the clustering. In the code, the default clustering variables are the normalised x,y coordinates, which would create clusters of user sessions based only on their mouse movements within each session.

However, if the client wants to cluster not only on mouse movements but, for example, clicking as well as movement or timing of mouse actions, then those clustering variables can be included in the clusteringOfSessions function. In a few weeks we will share a more in depth clustering algorithm of our own to give a deeper understanding of how clustering works.

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