Adaptive Self-Sufficient Itemset Miner for Transactional Data Streams

Abstract

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.

Publication
In The 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI)
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.
Click the Slides button above to demo Academic’s Markdown slides feature.

Supplementary notes can be added here, including code and math.