Mining surprising patterns using temporal description length

Summary: Proposes a coding-length based notion of surprising temporal patterns in market baskets, beyond frequent itemsets. Surprise equals the bits to encode a basket sequence under a scheme that rewards steady correlations and flags time-varying ones, with no parameters to tune. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 8523
Venue: VLDB
Year: 1998
Pagerank: 7.0419748e-05
Overall Rank: 3,991 | 72.27%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,125	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00012163978
3,726	Detecting Change in Data Streams	2004	VLDB	7.2348365e-05
6,220	A Regression-Based Temporal Pattern Mining Scheme for Data Streams	2003	VLDB	6.0117255e-05
7,086	A Framework for Measuring Changes in Data Characteristics	1999	PODS	5.7599404e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
704	Beyond Market Baskets: Generalizing Association Rules to Correlations	1997	SIGMOD	0.00014918974
1,314	Querying Shapes of Histories	1995	VLDB	0.00011293814

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