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OPP-Miner: Order-preserving sequential pattern mining for time series
Traditional sequential pattern mining methods were designed for symbolic sequence. As a
collection of measurements in chronological order, a time series needs to be discretized into …
collection of measurements in chronological order, a time series needs to be discretized into …
COPP-Miner: Top-k Contrast Order-Preserving Pattern Mining for Time Series Classification
Recently, order-preserving pattern (OPP) mining, a new sequential pattern mining method,
has been proposed to mine frequent relative orders in a time series. Although frequent …
has been proposed to mine frequent relative orders in a time series. Although frequent …
ONP-Miner: One-off negative sequential pattern mining
Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike
positive SPM, negative SPM can discover events that should have occurred but have not …
positive SPM, negative SPM can discover events that should have occurred but have not …
A new tree-based approach to mine sequential patterns
Generic sequential pattern mining problem aims to mine the set of sequential patterns from a
sequential database that satisfies a minimum support or occurrence threshold constraint …
sequential database that satisfies a minimum support or occurrence threshold constraint …
MCoR-Miner: Maximal co-occurrence nonoverlap** sequential rule mining
Y Li, C Zhang, J Li, W Song, Z Qi… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The aim of sequential pattern mining (SPM) is to discover potentially useful information from
a given sequence. Although various SPM methods have been investigated, most of these …
a given sequence. Although various SPM methods have been investigated, most of these …
OPR-Miner: Order-preserving rule mining for time series
Discovering frequent trends in time series is a critical task in data mining. Recently, order-
preserving matching was proposed to find all occurrences of a pattern in a time series …
preserving matching was proposed to find all occurrences of a pattern in a time series …
BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images
Z Cao, J Huang, X He, Z Zong - Knowledge-Based Systems, 2022 - Elsevier
During the past two years, a highly infectious virus known as COVID-19 has been damaging
and harming the health of people all over the world. Simultaneously, the number of patients …
and harming the health of people all over the world. Simultaneously, the number of patients …
RNP-Miner: Repetitive nonoverlap** sequential pattern mining
M Geng, Y Wu, Y Li, J Liu… - … on Knowledge and …, 2023 - ieeexplore.ieee.org
Sequential pattern mining (SPM) is an important branch of knowledge discovery that aims to
mine frequent sub-sequences (patterns) in a sequential database. Various SPM methods …
mine frequent sub-sequences (patterns) in a sequential database. Various SPM methods …
Co-occurrence order-preserving pattern mining with keypoint alignment for time series
Recently, order-preserving pattern (OPP) mining has been proposed to discover some
patterns, which can be seen as trend changes in time series. Although existing OPP mining …
patterns, which can be seen as trend changes in time series. Although existing OPP mining …
NetDPO:(delta, gamma)-approximate pattern matching with gap constraints under one-off condition
Approximate pattern matching not only is more general than exact pattern matching, but also
allows some data noise. Most of them adopt the Hamming distance to measure similarity …
allows some data noise. Most of them adopt the Hamming distance to measure similarity …