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Mining frequent episodes aims at recovering sequential patterns from temporaldata sequences, which can then be used to predict the occurrence of relatedevents in advance. On the other hand, gradual patterns that captureco-variation of complex attributes in the form of " when X increases/decreases,Y increases/decreases" play an important role in many real world applicationswhere huge volumes of complex numerical data must be handled. Recently, thesepatterns have received attention from the data mining community exploringtemporal data who proposed methods to automatically extract gradual patternsfrom temporal data. However, to the best of our knowledge, no method has beenproposed to extract gradual patterns that regularly appear at identical timeintervals in many sequences of temporal data, despite the fact that suchpatterns may add knowledge to certain applications, such as e-commerce. In thispaper, we propose to extract co-variations of periodically repeating attributesfrom the sequences of temporal data that we call seasonal gradual patterns. Forthis purpose, we formulate the task of mining seasonal gradual patterns as theproblem of mining periodic patterns in multiple sequences and then we exploitperiodic pattern mining algorithms to extract seasonal gradual patterns. Wediscuss specific features of these patterns and propose an approach for theirextraction based on mining periodic frequent patterns common to multiplesequences. We also propose a new anti-monotonous support definition associatedto these seasonal gradual patterns. The illustrative results obtained from somereal world data sets show that the proposed approach is efficient and that itcan extract small sets of patterns by filtering numerous nonseasonal patternsto identify the seasonal ones.

Extracting Seasonal Gradual Patterns from Temporal Sequence Data Using Periodic Patterns Mining