Fast deterministic algorithm for EEE components classification

Kazakovtsev, Lev; Антамошкин, А. Н.; Masich, I S

doi:10.1088/1757-899x/94/1/012015

Cited by 6 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2022), as well as from samples of industrial products (Kazakovtsev et al, 2015;Shkaberina et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Approach to Data Clustering Based on Molecular Chemical Reactions With Various Distance Measures

Markushin¹

2023

European Proceedings of Computers and Technology

View full text Add to dashboard Cite

Automatic clustering involves dividing a set of objects into subsets so that the objects from one subset are more similar to each other than to the objects from other subsets according to some criterion. The paper proposes an algorithm for clustering data using the k-means algorithm combined with molecular chemical reactions and with various types of distance measures: Euclidean distance, Squared Euclidean distance, Manhattan distance. This approach mimics a chemical reaction process in which reactants interact with one another. Every chemical reaction process generate a new molecular structure in the environment. By molecular structure, we mean a possible solution to data clustering, by optimizing the molecular chemical reactions we mean optimizing the results of data clustering (search for a global optimal solution). The solution obtained with k-means is used as an initial molecular structure solution to optimize chemical reactions by generating new solutions: single-molecule collision, single-molecule decomposition, intermolecular collision, and intermolecular synthesis. Computational experiments demonstrate the comparative efficiency and accuracy of using the k-means algorithm combined with molecular chemical reactions.

show abstract

“…2022), as well as from samples of industrial products (Kazakovtsev et al, 2015;Shkaberina et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

Approach to Data Clustering Based on Molecular Chemical Reactions With Various Distance Measures

Markushin¹

2023

European Proceedings of Computers and Technology

View full text Add to dashboard Cite

show abstract

“…Finally, an compromise variant is possible, in which the sets are partially combined: the first parent set being taken completely, with a random number of elements chosen randomly from the second set [17,20]. These heuristic procedures, which are (not in the strict sense) algorithms of local search in the neighborhood of known ("parent") solution represented by the set S can be embedded in various global search strategies.…”

Section: And Improve It With Algorithm 1 End Domentioning

confidence: 99%

VNS-Based Algorithms for the Centroid-Based Clustering Problem

Rozhnov¹,

Orlov²,

Kazakovtsev³

2019

FU Math Inform

View full text Add to dashboard Cite

The k-means algorithm with the corresponding problem formulation is one of the ﬁrst methods that researchers use when solving a new automatic grouping (clus-tering) problem. Its improvement, modiﬁcation and combination with other algorithms are described in the works of many researchers. In this research, we propose new al-gorithms of the Greedy Heuristic Method, which use an idea of the search in variable neighborhoods for solving the classical cluster analysis problem, and allows us to obtain a more accurate and stable result of solving in comparison with the known algorithms. Our computational experiments show that the new algorithms allow us to obtain re-sults with better values of the objective function value (sum of squared distances) in comparison with classical algorithms such as k-means, j-means and genetic algorithms on various practically important datasets. In addition, we present the ﬁrst results for the GPU realization of the Greedy Heuristic Method.

show abstract

“…In the field of data clustering of electronic radio products, an algorithm of this kind is the k-means algorithm proposed primarily by Lloyd (1982). Kazakovtsev et al (2015) continued research in this area related to the choice of data normalization method, the choice of the distance metric and the choice of the centroid initialization method. The algorithm of Milligan and Cooper (1985) depends on giving a priori the number of clusters and finds all groups to cluster at the same time.…”

Section: Introductionmentioning

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

Fast deterministic algorithm for EEE components classification

Cited by 6 publications

References 12 publications

Approach to Data Clustering Based on Molecular Chemical Reactions With Various Distance Measures

Approach to Data Clustering Based on Molecular Chemical Reactions With Various Distance Measures

VNS-Based Algorithms for the Centroid-Based Clustering Problem

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

Contact Info

Product

Resources

About