The Algorithms of Updating Sequential Patterns
Qingguo Zheng, Ke Xu, Shilong Ma, Weifeng Lv
Abstract
Because the data being mined in the temporal database will evolve with time, many researchers have focused on the incremental mining of frequent sequences in temporal database. In this paper, we propose an algorithm called IUS, using the frequent and negative border sequences in the original database for incremental sequence mining. To deal with the case where some data need to be updated from the original database, we present an algorithm called DUS to maintain sequential patterns in the updated database. We also define the negative border sequence threshold: Minnbdsupp to control the number of sequences in the negative border.
Create a lesson
Related papers
Same Query, Different Answer: Run-to-Run Variance in Semantic Query Processing
Oktie Hassanzadeh, Dharmashankar Subramanian
ProvQML: Efficient Provenance Capture for Machine Learning as Recursive Aggregate SQL [Extended Version]
Nkechi J. Akinwale, Seokki Lee
Benchmarking Content-Defined Chunking Algorithms for Data Deduplication
Marcel Gregoriadis, Leonhard Balduf, Björn Scheuermann et al.
Time-Varying Favorite-Longshot Bias in Polymarket Weather Markets
Shouchen Qiu, Huaijun Qiu
MatQuery: Execution-Grounded Natural-Language Querying over Heterogeneous Materials Databases
Chen Jason Zhang, Qi Chen
CALQA: Conformal Screening of Forecast-Ensemble Configurations from One Mergeable State
Xiufeng Liu, Ruyu Liu, Yusen Liu et al.