Characterizing Transactional Databases for Frequent Itemset Mining
This work addresses the need for reliable benchmarking in data mining by characterizing datasets, but it is incremental as it builds on existing metrics and studies.
The paper studied the characteristics of transactional databases used in frequent itemset mining to assess their diversity and representativeness for benchmarking, showing that their proposed metrics capture dataset complexity and providing a set of representative datasets for safe benchmarking.
This paper presents a study of the characteristics of transactional databases used in frequent itemset mining. Such characterizations have typically been used to benchmark and understand the data mining algorithms working on these databases. The aim of our study is to give a picture of how diverse and representative these benchmarking databases are, both in general but also in the context of particular empirical studies found in the literature. Our proposed list of metrics contains many of the existing metrics found in the literature, as well as new ones. Our study shows that our list of metrics is able to capture much of the datasets' inner complexity and thus provides a good basis for the characterization of transactional datasets. Finally, we provide a set of representative datasets based on our characterization that may be used as a benchmark safely.