Adaptive Self-Sufficient Itemset Miner for Transactional Data Streams


Most studies on pattern mining consider itemsets that have a high frequency of occurrence as useful, often determined by the support of the itemsets. However, current research has shown that we need to move beyond a pure “support-confidence” framework for pattern mining. In our research we will concentrate on detecting self-sufficient itemsets from data streams. These patterns have a frequency that is significantly different from the frequency of their subsets and supersets. We present a comprehensive framework for mining self-sufficient itemsets from data streams along with a drift detector. This supports mining self-sufficient itemsets in an online environment and provides the ability to adapt to changes in the stream. Our experimental evaluations show that our framework can mine self-sufficient itemsets faster in an online environment and with better precision and recall.

In The 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI)
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