Discovering frequent behaviors: time is an essential element of the context
One of the most popular problems in usage mining is the discovery of frequent behaviors. It relies on the extraction of frequent itemsets from usage databases. However, those databases are usually considered as a whole, and therefore, itemsets are extracted over the entire set of records. Our claim is that possible subsets, hidden within the structure of the data and containing relevant itemsets, may exist. These subsets, as well as the itemsets they contain, depend on the context. Time is an essential element of the context. The users' intents will differ from one period to another. Behaviors over Christmas will be different from those extracted during the summer. Unfortunately, these periods might be lost because of arbitrary divisions of the data. The goal of our work is to find itemsets that are frequent over a specific period, but would not be extracted by traditional methods since their support is very low over the whole dataset. We introduce the definition of solid itemsets, which represent coherent and compact behaviors over specific periods, and we propose Sim, an algorithm for their extraction.
itemsets periods time-aware