Incremental sequential patterns for multivariate temporal association rules mining

Most temporal association rule mining algorithms can mine the temporal relationship between items, but the sequential relations between successive items remain unknown, while sequential pattern mining algorithms can discover the sequential relationship between successive items but the quantitative time interval of sequential patterns remains unknown. Furthermore, they only focus on mining a historical temporal database, which ignores the fact that temporal databases are continually appended or updated. To address these problems, by integrating the advantages of these two algorithms, we extend the sequential pattern mining algorithm- PrefixSpan by introducing the concept of fuzzy temporal pattern and incremental learning, a new algorithm called Incremental sequential patterns for multivariate temporal association rules mining (ISPTAR) is proposed. First, the temporal transaction dataset obtained by fuzzy discretization of multivariate time series is converted into a temporal sequence dataset. Second, based on the PrefixSpan algorithm, the fuzzy temporal sequential patterns are mined by taking the valid time interval of the sequential pattern and the temporal relationship between items in the sequential pattern into account. Third, fuzzy temporal association rules can be constructed to mine the association between different attributes based on the mined fuzzy temporal sequential patterns. In addition, when new data are added, the proposed algorithm can update the sequential patterns without rescanning the historical database. We compare the ISPTAR algorithm with other sequential patterns mining and association rules mining algorithms on four real datasets from the UCI machine learning data repository. Experimental results demonstrate that ISPTAR, in most cases, outperformed the other algorithms in execution time and the number of generated rules. In addition, the rules generated by ISPTAR contain more temporal information, which is helpful to assist important decision-making.