This paper introduces an approach for incremental maximal frequent pattern (MFP) mining in sparse binary data, where instances are observed one by one. For this purpose, we propose the Augmented Itemset Tree (AIST), a data structure that incorporates features of the FP-tree into the itemset tree. In the given setting, we assume that just the index structure is maintained in main memory, and each instance is observed only once. The AIST not only stores observed frequent patterns, but also allows for quick frequency updates of relevant subpatterns. In order to quickly identify the current set of exact MFPs, potential candidates are extracted from former MFPs and patterns that occur in the new instance. The presented approach is evaluated oncerning the runtime and memory requirements depending on the number of instances, minimum support and different settings of pattern properties. The obtained results suggest that AISTs are useful for mining maximal frequent itemsets in an online setting whenever larger patterns can be expected.     «

This paper introduces an approach for incremental maximal frequent pattern (MFP) mining in sparse binary data, where instances are observed one by one. For this purpose, we propose the Augmented Itemset Tree (AIST), a data structure that incorporates features of the FP-tree into the itemset tree. In the given setting, we assume that just the index structure is maintained in main memory, and each instance is observed only once. The AIST not only stores observed frequent patterns, but also allows...     »