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: 8522
Venue: VLDB
Year: 1998
Pagerank: 6.6583221e-05
Overall Rank: 3,894 | 72.92%
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
1,253	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00013032074
3,685	Detecting Change in Data Streams	2004	VLDB	6.8448674e-05
6,342	A Regression-Based Temporal Pattern Mining Scheme for Data Streams	2003	VLDB	5.1034654e-05
6,544	A Framework for Measuring Changes in Data Characteristics	1999	PODS	5.0202405e-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
744	Beyond Market Baskets: Generalizing Association Rules to Correlations	1997	SIGMOD	0.00017333019
1,331	Querying Shapes of Histories	1995	VLDB	0.00012546163

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