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

Stefanowski J., Ziembiński R.

Control and Cybernetics

2009

Vol. 38, no 1

27-45

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.

Algorithms for Context Based Sequential Pattern Mining

Ziembiński R.

Fundamenta Informaticae

2007

Vol. 76, nr 4

495-510

This paper describes practical aspects of a novel approach to the sequential pattern mining named Context Based Sequential Pattern Mining (CBSPM). It introduces a novel ContextMapping algorithm used for the context pattern mining and an illustrative example showing some advantages of the proposed method. The approach presented here takes into consideration some shortcomings of the classic problem of the sequential pattern mining. The significant advantage of the classic sequential patterns mining is simplicity. It introduces simple element construction, built upon set of atomic items. The comparison of sequence's elements utilizes simple inclusion of sets. But many practical problems like web event mining, monitoring, tracking and rules generation often require mining more complex data. The CBSPM takes into account non nominal attributes and similarity of sequence's elements. An approach described here extends traditional problem adding a vector of context attributes of any kind to sequences and sequence’s elements. Context vectors contain details about sequence's and element's origin. The mining process results in context patterns containing additional, valuable context information useful in interpretation of patterns origin.