Nasdaq utvecklar och tillhandahåller mjukvarulösningar för clearinghus. Det finns ett intresse för att utveckla en fördjupad förståelse för hur funktionaliteten av produkten används. En möjlighet för detta är att använda sig av hierarkisk klustring av klickströmmar från webbgränssnittet. Denna rapport utvecklar ett tillvägagångsätt för en sådan klustring och tillämpar den på ett redan befintligt dataset av klickströmsloggar. Att använda sig av ett euklidiskt avståndsmått kan fungera för enklare klustringar så som gruppering av produktsidor. För en djupare analys av användarbeteendet genom en klustring av sessioner ger dock Damerau-Levenshtein bättre resultat då det även tar hänsyn till i vilken ordningsföljd sidvisningarna för respektive session sker. / Nasdaq develops and provides software solutions for clearing houses. There is an interest in developing an in-depth understanding of how the functionality of this product is used. One possibility for this is to use hierarchical clustering of click streams from the web interface. This report develops a methodology for such clustering and applies it to an already existing dataset of clickstream logs. Using a Euclidean distance measure can work for simpler clusters such as grouping product pages. For a deeper analysis of user behavior through a clustering of sessions, however, Damerau–Levenshtein gives better results as it also takes into account the order of the pages visited within the sessions.

The purpose of this study was to develop an interactive tool that enables identification of different types of users of an application based on clickstream data. A complex hierarchical clustering algorithm tool called Recursive Hierarchical Clustering (RHC) was used. RHC provides a visualisation of user types as clusters, where each cluster has its own distinguishing action pattern, i.e., one or several consecutive actions made by the user in the application. A case study was conducted on the mobile application Plick, which is an application for selling and buying second hand clothes. During the course of the project, the analysis and its result was discovered to be difficult to understand by the operators of the tool. The interactive tool had to be extended to visualise the complex analysis and its result in an intuitive way. A literature study of how humans interpret information, and how to present it to operators, was conducted and led to a redesign of the tool. More information was added to each cluster to enable further understanding of the clustering results. A clustering reconfiguration option was also created where operators of the tool got the possibility to interact with the analysis. In the reconfiguration, the operator could change the input file of the cluster analysis and thus the end result. Usability tests showed that the extra added information about the clusters served as an amplification and a verification of the original results presented by RHC. In some cases the original result presented by RHC was used as a verification to user group identification made by the operator solely based on the extra added information. The usability tests showed that the complex analysis with its results could be understood and configured without considerable comprehension of the algorithm. Instead it seemed like it could be successfully used in order to identify user types with help of visual clues in the interface and default settings in the reconfiguration. The visualisation tool is shown to be successful in identifying and visualising user groups in an intuitive way.