scholarly journals Cascading Spatio-temporal Pattern Discovery: A Summary of Results

2010 ◽  
Author(s):  
Pradeep Mohan ◽  
Shashi Shekhar ◽  
James A. Shine ◽  
James P. Rogers
Keyword(s):  
Temporal Pattern ◽  
Pattern Discovery ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery

scholarly journals Cascading Spatio-Temporal Pattern Discovery

2012 ◽  
Vol 24 (11) ◽  
pp. 1977-1992 ◽  
Author(s):  
Pradeep Mohan ◽  
Shashi Shekhar ◽  
James A. Shine ◽  
James P. Rogers
Keyword(s):  
Temporal Pattern ◽  
Pattern Discovery ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery

Spatio-temporal Pattern discovery in sensor data: A multivalued decision systems approach

2016 ◽  
Vol 109 ◽  
pp. 137-146 ◽  
Author(s):  
Sanchita Mal-Sarkar ◽  
Iftikhar U. Sikder ◽  
Vijay K. Konangi
Keyword(s):  
Temporal Pattern ◽  
Systems Approach ◽  
Pattern Discovery ◽  
Sensor Data ◽  
Decision Systems ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery

Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling

2016 ◽  
Vol 28 (4) ◽  
pp. 979-993 ◽  
Author(s):  
Chung-Hsien Yu ◽  
Wei Ding ◽  
Melissa Morabito ◽  
Ping Chen
Keyword(s):  
Predictive Modeling ◽  
Temporal Pattern ◽  
Pattern Discovery ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery

scholarly journals Heuristic Search Based Feature Selection and Discretive Self-Organized Map Clustering for Spatio-Temporal Pattern Discovery

2019 ◽  
Vol 8 (10S) ◽  
pp. 197-207
Keyword(s):  
Temporal Pattern ◽  
Predictive Analytics ◽  
False Positive Rate ◽  
Pattern Discovery ◽  
Temporal Data ◽  
Temporal Features ◽  
Best First Search ◽  
Spatio Temporal ◽  
Temporal Pattern Discovery ◽  
Self Organizing

Spatio-temporal pattern discovery is an essential one in data mining for predictive analytics. Since it manages both space and time information depending on their characteristics and the preferred applications performances. The predictive analytics uses the Spatio-temporal features to discover future outcomes. The several works have been done in the Spatio-temporal pattern discovery. But the accurate pattern discovery is the major challenges. In order to improve the accurate pattern discovery, Heuristic Best-First Search based Discretized Self-Organizing Feature Map (HBFS-DSOFM) Model is introduced. The HBFS-DSOFM model comprises two processes namely, Spatio-temporal feature selection and clustering. Initially, the Heuristic Best-First Search Algorithm is used for selecting the relevant Spatio-temporal features from the large dataset for pattern discovery. Best-first search explores a decision tree for selecting the relevant Spatio-temporal features through the maximum information gain value. After that, the Spatio-temporal data are clustered with the selected features by using Discretized Self-Organizing Feature Mapping Algorithm for Spatio-temporal pattern discovery. In Discretized Self-Organizing Feature Mapping, input spatio-temporal data is connected to the prototype neurons through the synaptic weight. For the clustering process, weights of the neurons (i.e. cluster) are initialized with random values. After that, the Manhattan distance is used to compute the distance between the input vector and cluster weight value. The gradient descent is applied to discover closest distance. The cluster whose weight is closest to the input data is grouped into the particular cluster. Then the weight of the cluster is updated with the previous weight value for grouping the entire data. This clustering process gets iterated until it satisfies termination condition. Finally, the outputs of Spatio-temporal data are combined to form a spatio-temporal pattern for efficient predictive analytics. Experimental evaluation is carried out for El Nino Dataset and taxi trajectory dataset using the factors such as time complexity, clustering accuracy, and false positive rate. The results confirm that the proposed HBFS-DSOFM model increases the Spatio-temporal pattern discovery in terms of high clustering accuracy with a less false positive rate as well as minimum time complexity. Based on the clarification, HBFS-DSOFM model is more efficient than the state-of-the-art methods.

Empirical Performance of the Calibrated Self-Controlled Cohort Analysis Within Temporal Pattern Discovery: Lessons for Developing a Risk Identification and Analysis System

Drug Safety ◽  
2013 ◽  
Vol 36 (S1) ◽  
pp. 107-121 ◽  
Author(s):  
G. Niklas Norén ◽  
Tomas Bergvall ◽  
Patrick B. Ryan ◽  
Kristina Juhlin ◽  
Martijn J. Schuemie ◽  
...  
Keyword(s):  
Temporal Pattern ◽  
Cohort Analysis ◽  
Pattern Discovery ◽  
Risk Identification ◽  
Empirical Performance ◽  
Analysis System ◽  
Temporal Pattern Discovery

Enhancing Anomaly Detection Using Temporal Pattern Discovery

2009 ◽  
pp. 175-194 ◽  
Author(s):  
Vikramaditya R. Jakkula ◽  
Aaron S. Crandall ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Temporal Pattern ◽  
Pattern Discovery ◽  
Temporal Pattern Discovery

scholarly journals Evaluating the performance of Temporal Pattern Discovery: New application using Statins and Rhabdomyolysis in OMOP databases

2020 ◽  
Author(s):  
M Lavallee ◽  
T Yu ◽  
L Evans ◽  
Mieke Van Hemelrijck ◽  
C Bosco ◽  
...  
Keyword(s):  
Signal Detection ◽  
Temporal Pattern ◽  
Pattern Discovery ◽  
Control Period ◽  
Data Sources ◽  
Health Improvement ◽  
Initial Exposure ◽  
Electronic Healthcare Databases ◽  
Useful Signal ◽  
Temporal Pattern Discovery

Abstract Background: Temporal Pattern Discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources. Methods: We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance. Results: Similar to previous findings, we noted an increase in the information component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1-30 days as compared to the control period of -180 to -1days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs. Conclusion: Our OMOP replication matched the results of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

Sign in / Sign up

Export Citation Format

Share Document