Influence of Learning Rates and Neighboring Functions on Self-Organizing Maps

Stefanovič, Pavel; Kurasova, Olga

doi:10.1007/978-3-642-21566-7_14

Cited by 13 publications

(14 citation statements)

References 9 publications

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“…Usually, linear, inverse-of-time, and power series learning rates are used for the SOM training (Stefanovič andKurasova, 2014, 2011). In this research, the learning rate is constant and equal to 0.5, both the initial neighbourhood radius and the radius decay parameter are set to −0.1.…”

Section: Clustering With Sommentioning

confidence: 99%

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

Venskus¹,

Treigys²,

Bernatavičienė³

et al. 2017

Informatica

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Abstract. In recent years, the growth of marine traffic in ports and their surroundings raise the traffic and security control problems and increase the workload for traffic control operators. The automated identification system of vessel movement generates huge amounts of data that need to be analysed to make the proper decision. Thus, rapid self-learning algorithms for the decision support system have to be developed to detect the abnormal vessel movement in intense marine traffic areas. The paper presents a new self-learning adaptive classification algorithm based on the combination of a self-organizing map (SOM) and a virtual pheromone for abnormal vessel movement detection in maritime traffic. To improve the quality of classification results, Mexican hat neighbourhood function has been used as a SOM neighbourhood function. To estimate the classification results of the proposed algorithm, an experimental investigation has been performed using the real data set, provided by the Klaipėda seaport and that obtained from the automated identification system. The results of the research show that the proposed algorithm provides rapid self-learning characteristics and classification.

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Section: Clustering With Sommentioning

confidence: 99%

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

Venskus¹,

Treigys²,

Bernatavičienė³

et al. 2017

Informatica

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“…Here t is the number of a learning step, h w ij is a neighboring function [28], w is a pair of indices of the neuron-winner of vector X p . The learning is repeated until the maximum number of learning steps is attained.…”

Section: Related Work 21 Self-organizing Mapmentioning

confidence: 99%

Outlier Detection in Self-Organizing Maps and Their Quality Estimation

Stefanovič¹,

Kurasova²

2018

NNW

Self Cite

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In the paper, an algorithm that allows to detect and reject outliers in a self-organizing map (SOM) has been proposed. SOM is used for data clustering as well as dimensionality reduction and the results obtained are presented in a special graphical form. To detect outliers in SOM, a genetic algorithm-based travelling salesman approach has been applied. After outliers are detected and removed, the SOM quality has to be estimated. A measure has been proposed to evaluate the coincidence of data classes and clusters obtained in SOM. A larger value of the measure means that the distance between centers of different classes in SOM is longer and the clusters corresponding to the data classes separate better. With a view to illustrate the proposed algorithm, two datasets (numerical and textual) are used in this investigation.

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“…Offspring -the chromosome of parent 1 and 2 recombination: points "o1" and "o2" are taken from parent 1, while points "o3" and "o4" -from parent 2 Other authors also carried out experiments with glass data. In paper (Stefanovič, Kurasova 2011) glass data is analyzed using artificial neural networks and observed how glasses are distributed into classes while teaching neural networks. K. Ząbkiewicz (2013) offers to combine principal component analysis and evolutional methods.…”

Section: Experimental Investigationmentioning

confidence: 99%

Genetic Algorithms for Multidimensional Scaling / Genetinių Algoritmų Taikymas Daugiamatėms Skalėms

Dzidolikaitė

2015

Mokslas – Lietuvos Ateitis

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The paper analyzes global optimization problem. In order to solve this problem multidimensional scaling algorithm is combined with genetic algorithm. Using multidimensional scaling we search for multidimensional data projections in a lower-dimensional space and try to keep dissimilarities of the set that we analyze. Using genetic algorithms we can get more than one local solution, but the whole population of optimal points. Different optimal points give different images. Looking at several multidimensional data images an expert can notice some qualities of given multidimensional data. In the paper genetic algorithm is applied for multidimensional scaling and glass data is visualized, and certain qualities are noticed. Analizuojamas globaliojo optimizavimo uždavinys. Jis apibrėžiamas kaip netiesinės tolydžiųjų kintamųjų tikslo funkcijos optimizavimas leistinojoje srityje. Optimizuojant taikomi įvairūs algoritmai. Paprastai taikant tikslius algoritmus randamas tikslus sprendinys, tačiau tai gali trukti labai ilgai. Dažnai norima gauti gerą sprendinį per priimtiną laiko tarpą. Tokiu atveju galimi kiti – euristiniai, algoritmai, kitaip dar vadinami euristikomis. Viena iš euristikų yra genetiniai algoritmai, kopijuojantys gyvojoje gamtoje vykstančią evoliuciją. Sudarant algoritmus naudojami evoliuciniai operatoriai: paveldimumas, mutacija, selekcija ir rekombinacija. Taikant genetinius algoritmus galima rasti pakankamai gerus sprendinius tų uždavinių, kuriems nėra tikslių algoritmų. Genetiniai algoritmai taip pat taikytini vizualizuojant duomenis daugiamačių skalių metodu. Taikant daugiamates skales ieškoma daugiamačių duomenų projekcijų mažesnio skaičiaus matmenų erdvėje siekiant išsaugoti analizuojamos aibės panašumus arba skirtingumus. Taikant genetinius algoritmus gaunamas ne vienas lokalusis sprendinys, o visa optimumų populiacija. Skirtingi optimumai atitinka skirtingus vaizdus. Matydamas kelis daugiamačių duomenų variantus, ekspertas gali įžvelgti daugiau daugiamačių duomenų savybių. Straipsnyje genetinis algoritmas pritaikytas daugiamatėms skalėms. Parodoma, kad daugiamačių skalių algoritmą galima kombinuoti su genetiniu algoritmu ir panaudoti daugiamačiams duomenims vizualizuoti.

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Influence of Learning Rates and Neighboring Functions on Self-Organizing Maps

Cited by 13 publications

References 9 publications

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

Integration of a Self-Organizing Map and a Virtual Pheromone for Real-Time Abnormal Movement Detection in Marine Traffic

Outlier Detection in Self-Organizing Maps and Their Quality Estimation

Genetic Algorithms for Multidimensional Scaling / Genetinių Algoritmų Taikymas Daugiamatėms Skalėms

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