An experimental evaluation of two approaches to mining context based sequential patterns

The paper discusses the results of experiments with a new context extension of a sequential pattern mining problem. In this extension, two kinds of context attributes are introduced for describing the source of a sequence and for each element inside this sequence. Such context based sequential patterns may be discovered by a new algorithm, called Context Mapping Improved, specific for handling attributes with similarity functions. For numerical attributes an alternative approach could include their pre-discretization, transforming discrete values into artificial items and, then, using an adaptation of an algorithm for mining sequential patterns from nominal items. The aim of this paper is to experimentally compare these two approaches to mine artificially generated sequence databases with numerical context attributes where several reference patterns are hidden. The results of experiments show that the Context Mapping Improved algorithm has led to better re-discovery of reference patterns. Moreover, a new measure for comparing two sets of context based patterns is introduced.