[PDF][PDF] Modern applications and challenges for rare itemset mining
Data mining is the process of extracting useful unknown knowledge from large datasets.
Frequent itemset mining is the fundamental task of data mining that aims at discovering …
Frequent itemset mining is the fundamental task of data mining that aims at discovering …
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: Nonoverlapping maximal sequential pattern mining
Nonoverlapping 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 …
Chronic disease prediction using administrative data and graph theory: The case of type 2 diabetes
Clinical diagnosis and regular monitoring of the population at risk of chronic diseases is
clinically and financially resource-intensive. Mining administrative data could be an effective …
clinically and financially resource-intensive. Mining administrative data could be an effective …
A novel association rule mining method for the identification of rare functional dependencies in complex technical infrastructures from alarm data
This work presents a data-driven method for identifying rare functional dependencies among
components of different systems of Complex Technical Infrastructures (CTIs) from large …
components of different systems of Complex Technical Infrastructures (CTIs) from large …
An interactive human centered data science approach towards crime pattern analysis
The traditional machine learning systems lack a pathway for a human to integrate their
domain knowledge into the underlying machine learning algorithms. The utilization of such …
domain knowledge into the underlying machine learning algorithms. The utilization of such …
The confounding role of common diabetes medications in developing acute renal failure: A data mining approach with emphasis on drug-drug interactions
B Davazdahemami, D Delen - Expert Systems with Applications, 2019 - Elsevier
Longstanding diabetes mellitus is today known as the primary reason for kidney failure in
the patients having that condition. While the prior research has studied the confounding role …
the patients having that condition. While the prior research has studied the confounding role …
A regionally scalable habitat typology for assessing benthic habitats and fish communities: Application to New Caledonia reefs and lagoons
D Pelletier, N Selmaoui‐Folcher, T Bockel… - Ecology and …, 2020 - Wiley Online Library
Scalable assessments of biodiversity are required to successfully and adaptively manage
coastal ecosystems. Assessments must account for habitat variations at multiple spatial …
coastal ecosystems. Assessments must account for habitat variations at multiple spatial …
Applying mutual information for discretization to support the discovery of rare-unusual association rule in cerebrovascular examination dataset
CP Wulandari, C Ou-Yang, HC Wang - Expert Systems with Applications, 2019 - Elsevier
In knowledge discovery studies, association rules mining has been extensively studied to
discover hidden knowledge and relationships among set of items in a transactional dataset …
discover hidden knowledge and relationships among set of items in a transactional dataset …
Efficient Generalized Temporal Pattern Mining in Time Series Using Mutual Information
Big time series are increasingly available from an ever wider range of IoT-enabled sensors
deployed in various environments. Significant insights can be gained by mining temporal …
deployed in various environments. Significant insights can be gained by mining temporal …