|
Title
|
|
|
|
Mining top-k quantile-based cohesive sequential patterns
|
|
|
Author
|
|
|
|
|
|
|
Abstract
|
|
|
|
| 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 efficient 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 efficient 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 state-of-the-art methods for sequential pattern mining and show that our algorithm is efficient and produces qualitatively interesting patterns on large event sequences. |
|
|
|
Language
|
|
|
|
English
|
|
|
Source (book)
|
|
|
|
Proceedings of the 2018 SIAM International Conference on Data Mining, May 3-5, 2018, San Diego, CA, USA
|
|
|
Publication
|
|
|
|
San Diego, Calif.
:
SIAM
,
2018
|
|
|
ISBN
|
|
|
|
978-1-61197-532-1
|
|
|
DOI
|
|
|
|
10.1137/1.9781611975321.11
|
|
|
Volume/pages
|
|
|
|
p. 90-98
|
|
|
Full text (Publisher's DOI)
|
|
|
|
|
|
|
Full text (publisher's version - intranet only)
|
|
|
|
|
|