Mining Top-k Quantile-based Cohesive Sequential Patterns

Finding patterns in long event sequences is an important data mining task. Two decades ago research focused on finding all frequent patterns, where the anti-monotonic property of support was used to design e cient algorithms. Recent research focuses on producing a smaller output containing only the most interesting patterns. To achieve this goal, we introduce a new interestingness measure by computing the proportion of the occurrences of a pattern that are cohesive. This measure is robust to outliers, and is applicable to sequential patterns. We implement an e cient algorithm based on constrained prefix-projected pattern growth and pruning based on an upper bound to uncover the set of top-k quantile-based cohesive sequential patterns. We run experiments to compare our method with existing stateof-the-art methods for sequential pattern mining and show that our algorithm is e cient and produces qualitatively interesting patterns on large event sequences.