Frequent pattern mining has been a focused theme in data mining research for over a
decade. Abundant literature has been dedicated to this research and tremendous progress …

Frequent item set mining is one of the best known and most popular data mining methods.
Originally developed for market basket analysis, it is used nowadays for almost any task that …

In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

One of the major problems in pattern mining is the explosion of the number of results. Tight
constraints reveal only common knowledge, while loose constraints lead to an explosion in …

Event sequence mining is often used to summarize patterns from hundreds of sequences
but faces special challenges when handling racket sports data. In racket sports (eg, tennis …

Spotting anomalies in large multi-dimensional databases is a crucial task with many
applications in finance, health care, security, etc. We introduce COMPREX, a new approach …

Recent research has highlighted the practical benefits of subjective interestingness
measures, which quantify the novelty or unexpectedness of a pattern when contrasted with …

We formalize the data mining process as a process of information exchange, defined by the
following key components. The data miner's state of mind is modeled as a probability …

Traffic flow pattern identification, as well as anomaly detection, is an important component
for traffic operations and control. To reveal the characteristics of regional traffic flow patterns …

Data analysis is an inherently iterative process. That is, what we know about the data greatly
determines our expectations, and hence, what result we would find the most interesting. With …