4.6 Article

An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

RT-GSOM: Rough tolerance growing self-organizing map

Anima Pramanik et al.

Summary: RT-GSOM is a novel algorithm that introduces the concept of rough tolerance set to reduce uncertainty in decision-making, address information loss and overlapping patterns of decision classes. By allowing the network to grow based on extracted data in an unsupervised manner, it demonstrates superior learning rate and cluster quality over other algorithms for both categorical and continuous data.

INFORMATION SCIENCES (2021)

Article Computer Science, Artificial Intelligence

How much can k-means be improved by using better initialization and repeats?

Pasi Franti et al.

PATTERN RECOGNITION (2019)

Article Engineering, Industrial

An optimization-based decision tree approach for predicting slip-trip-fall accidents at work

Sobhan Sarkar et al.

SAFETY SCIENCE (2019)

Article Computer Science, Information Systems

Spark-GHSOM: Growing Hierarchical Self-Organizing Map for large scale mixed attribute datasets

Ameya Malondkar et al.

INFORMATION SCIENCES (2019)

Article Computer Science, Information Systems

Tolerance rough fuzzy decision tree

Junhai Zhai et al.

INFORMATION SCIENCES (2018)

Article Computer Science, Artificial Intelligence

A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO

Seyed Mohsen Mousavi et al.

APPLIED SOFT COMPUTING (2016)

Review Computer Science, Artificial Intelligence

A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms

Parviz Fattahi et al.

APPLIED SOFT COMPUTING (2015)

Article Computer Science, Artificial Intelligence

Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks

Sinem Kulluk et al.

EXPERT SYSTEMS WITH APPLICATIONS (2013)

Article Automation & Control Systems

Assessing and classifying risk of pipeline third-party interference based on fault tree and SOM

Wei Liang et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2012)

Article Engineering, Civil

Modeling the risk of structural fire incidents using a self-organizing map

Ali Asgary et al.

FIRE SAFETY JOURNAL (2012)

Article Engineering, Industrial

Self-Organizing Map and clustering algorithms for the analysis of occupational accident databases

Federica Palamara et al.

SAFETY SCIENCE (2011)

Article Computer Science, Artificial Intelligence

Self organizing maps in corporate finance: Quantitative and qualitative analysis of debt and leasing

Eric Severin

NEUROCOMPUTING (2010)

Article Statistics & Probability

Measuring and testing dependence by correlation of distances

Gabor J. Szekely et al.

ANNALS OF STATISTICS (2007)

Article Computer Science, Artificial Intelligence

Growing Hierarchical Tree SOM: An unsupervised neural network with dynamic topology

Alberto Forti et al.

NEURAL NETWORKS (2006)

Article Computer Science, Artificial Intelligence

Web page clustering using a self-organizing map of user navigation patterns

KA Smith et al.

DECISION SUPPORT SYSTEMS (2003)

Article Computer Science, Artificial Intelligence

Clustering of the self-organizing map

J Vesanto et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2000)

Article Computer Science, Artificial Intelligence

Dynamic self-organizing maps with controlled growth for knowledge discovery

D Alahakoon et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2000)