Time Aware Mining of Itemsets

Frequent behavioural pattern mining is a very important topic of knowledge discovery, intended to extract correlations between items recorded in large databases or Web access logs. However, those databases are usually considered as a whole and hence, itemsets are extracted over the entire set of records. Our claim is that possible periods, hidden within the structure of the data and containing compact itemsets, may exist. These periods, as well as the itemsets they contain, might not be found by traditional data mining methods due to their very weak support. Furthermore, these periods might be lost depending on an arbitrary division 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. In this paper, we introduce the definition of solid itemsets, which represent a coherent and compact behavior over a specific period, and we propose SIM, an algorithm for their extraction. This work may find many applications in sensitive domains such as fraud or intrusion detection.