A generative pattern model for mining binary datasets
Claudio Lucchese, Salvatore Orlando, Raffaele Perego · 2010
In many application fields, huge binary datasets modeling real life-phenomena are daily produced. These datasets record observations of some events, and people are often interested in mining them in order to recognize recurrent patterns. However, the discovery of the most important patterns is very challenging. For example, these patterns may overlap, or be related only to a particular subset of the observations. Finally, the mining can be hindered by the presence of noise.