Impact of data normalization methods and clustering model in the problem of automatic grouping of industrial products

Ahmatshin, F. G.; Kazakotsev, L. A.

doi:10.1088/1742-6596/1679/3/032085

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the work of Ahmatshin and Kazakotsev (2020), the selected test data were normalized to the standard deviation and to the allowable values of the parameter.…”

Section: Methodsmentioning

confidence: 99%

Greedy Heuristics for the Choice of the Radius of Local Concentrations in FOREL-2 Algorithm

Ahmatshin¹

2023

European Proceedings of Computers and Technology

View full text Add to dashboard Cite

The authors examine the problem of choosing the search radius for local concentrations in the FOREL-2 clustering algorithm with an initial number of clusters. Our approach was aimed at improving the accuracy and stability of the result, such as identifying homogeneous batches of industrial products. We examined the k-means and FOREL-2 algorithms by using normalized standard deviation test values and by valid parameter values for the problem of automatic classification of objects in a multi-dimensional space of measured parameters. For such problems, with the use of the FOREL-2 algorithm, we apply greedy heuristic procedures to select the radius of local concentrations. According to the obtained Rand index, the approach which uses the FOREL-2 algorithm demonstrated the best accuracy with a larger value of the objective function in comparison with the k-means algorithm. The accuracy and speed of the software implementation of the algorithm are quite acceptable for solving the problem of clustering electronic radio products based on test data. The use of greedy heuristics for choosing the radius of the search for local concentrations in the FOREL-2 clustering algorithm with a specified number of clusters has an advantage in the speed of exact clustering compared to the k-means algorithm that uses greedy heuristics for choosing centroids.

show abstract

“…In the work of Ahmatshin and Kazakotsev (2020), the selected test data were normalized to the standard deviation and to the allowable values of the parameter.…”

Section: Methodsmentioning

confidence: 99%

Greedy Heuristics for the Choice of the Radius of Local Concentrations in FOREL-2 Algorithm

Ahmatshin¹

2023

European Proceedings of Computers and Technology

View full text Add to dashboard Cite

show abstract

Impact of data normalization methods and clustering model in the problem of automatic grouping of industrial products

Cited by 1 publication

References 4 publications

Greedy Heuristics for the Choice of the Radius of Local Concentrations in FOREL-2 Algorithm

Greedy Heuristics for the Choice of the Radius of Local Concentrations in FOREL-2 Algorithm

Contact Info

Product

Resources

About