HANP-Miner: High average utility nonoverlap** sequential pattern mining
Nonoverlap** sequential pattern mining (SPM) is a data analysis task, which aims at
identifying repetitive sequential patterns with gap constraint in a set of discrete sequences …
identifying repetitive sequential patterns with gap constraint in a set of discrete sequences …
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 …
NetNMSP: Nonoverlap** maximal sequential pattern mining
Nonoverlap** sequential pattern mining, as a kind of repetitive sequential pattern mining
with gap constraints, can find more valuable patterns. Traditional algorithms focused on …
with gap constraints, can find more valuable patterns. Traditional algorithms focused on …
STWD-SFNN: Sequential three-way decisions with a single hidden layer feedforward neural network
The three-way decisions strategy has been employed to construct network topology in a
single hidden layer feedforward neural network (SFNN). However, this model has a general …
single hidden layer feedforward neural network (SFNN). However, this model has a general …
Sequential pattern mining algorithms and their applications: a technical review
N Mazumdar, PKD Sarma - International Journal of Data Science and …, 2024 - Springer
Sequential pattern mining (SPM) is a useful tool for extracting implicit and meaningful rules
from sequence datasets that can aid the decision-making process. These rules are ordered …
from sequence datasets that can aid the decision-making process. These rules are ordered …
HW-Forest: Deep forest with hashing screening and window screening
As a novel deep learning model, gcForest has been widely used in various applications.
However, current multi-grained scanning of gcForest produces many redundant feature …
However, current multi-grained scanning of gcForest produces many redundant feature …
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 …
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 …
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 …
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 …