Accuracy study of data recovery technique based on fuzzy clustering

T.V. Afanasyeva; I.V. Sibirev

Accuracy study of data recovery technique based on fuzzy clustering

T.V. Afanasyeva¹, I.V. Sibirev²

research article UDC 004.8 Pages: 50-53

¹ Dr.Sc.(Eng.), Professor, Associate Professor, Department «Information Systems», Ulyanovsk State Technical University
² Post-graduate Student, Department «Information Systems», Ulyanovsk State Technical University

Keywords: data recovery algorithm fuzzy clustering estimate of data recovery

Abstract

The article describes and experimentally investigates the algorithm recovery of omissions in numerical data based on fuzzy clustering. The aim of the study is to obtain the accuracy of the algorithm on artificial data with different number of omissions. It is shown that the use of fuzzy clustering to fill data gaps has an advantage in accuracy in comparison with the algorithm based on the arithmetic mean.

How to cite

Афанасьева Т.В., Сибирев И.В. Исследование точности алгоритма восстановления данных на основе нечеткой кластеризации // Радиотехника. 2018. № 6. С. 50–53.

References

Mockus A. How to run empirical studies using project repositories. – Avaya Labs. 2006. URL = http://www.research.avayalabs.com /user/audris (ot 24.02.17).
Algoritm vosstanovleniya danny’x po fajlovy’m «signaturam». URL = https://spark.ru/startup/hetmansoftware/blog/9391 /algoritm-vosstanovleniya-dannih-po-fajlovim-signaturam (ot 4.04.18).
Xalafyan A.A. Statistica 6. Statisticheskij analiz danny’x: Uchebnik. M.: OOO «Binom-Press». 2007. 512 s.
Littl R.Dzh.A., Rubin D.B. Statisticheskij analiz danny’x s propuskami: Per. s angl. M.: Finansy’ i statistika.1990. 336 s.
Vyatchenin D.A., Nechetkie metody’ avtomaticheskoj klassifikaczii. Minsk: UP «Texnoprint». 2004. 219 s.
Busse M., Orbanz P., Buhmann M. Cluster Analysis of Heterogeneous Rank Data. URL = http://www.machinelearning.org/proceedings /icml2007/ papers/341.pdf (ot 24.02.17).
Masal’skix A.V. Parallel’ny’j algoritm odnogo metoda vosstanovleniya tablichny’x danny’x // Izvestiya Tul’skogo gosudarstvennogo universiteta. Estestvenny’e nauki. 2014. № 3. S. 67−77.
Rahman M.M., Davis D.N. Fuzzy Unordered Rules Induction Algorithm Used as Missing Value Imputation Methods for K-Mean Clustering on Real Cardiovascular Data. URL = http://www2.dcs.hull.ac.uk/NEAT/dnd/papers/ Fuzzy_InductionAlgorithm_MVI.pdf (ot 4.05.18).
Sibirev I.V., Afanas’eva T.V. Algoritm predobrabotki i vosstanovleniya anketny’x danny’x // Materialy’ VI Mezhdunar. nauch.-texn. konf. «Otkry’ty’e semanticheskie texnologii proektirovaniya intellektual’ny’x sistem» (Open Semantic Technologies for Intelligent Systems, OSTIS-2016). Minsk: BGUIR. 2016. S. 271−274.
Sibirev I.V. Predobrabotka danny’x v intellektual’nom analize na osnove vosstanovleniya propushhenny’x anketny’x znachenij // Trudy’ Pyatnadczatoj naczional’noj konf. po iskusstvennomu intellektu s Mezhdunar. uchastiem KII-2016. V 3-x tomax. Smolensk: Universum. 2016. T. 1. S. 378−386.
Sibirev I.V., Afanas’eva T.V. Analiz e’ffektivnosti algoritma vosstanovleniya anketny’x danny’x // Sb. nauchny’x trudov III Mezhdunar. nauchno-prakticheskaya konf. «E’lektronnoe obuchenie v neprery’vnom obrazovanii (E’ONO-2016)». Ul’yanovsk: UlGTU. 2016. 367−373 s.
Sibirev I.V. Programma generator isxodny’x danny’x dlya klasterizaczii / Nechetkie sistemy’ i myagkie vy’chisleniya. Promy’shlenny’e primeneniya // Sb. nauchny’x trudov IV Vseros. nauchno- prakticheskoj mul’tikonferenczii s Mezhdunar. uchastiem «Prikladny’e informaczionny’e sistemy’ (PIS-2017)». Ul’yanovsk (Rossiya). 29−31 maya 2017. Ul’yanovsk: UlGTU. 2017. S. 171−174.

Full text, PDF 350 ₽

[1] Mockus A. How to run empirical studies using project repositories. – Avaya Labs. 2006. URL = http://www.research.avayalabs.com /user/audris (ot 24.02.17).

[2] Algoritm vosstanovleniya danny’x po fajlovy’m «signaturam». URL = https://spark.ru/startup/hetmansoftware/blog/9391 /algoritm-vosstanovleniya-dannih-po-fajlovim-signaturam (ot 4.04.18).

[3] Xalafyan A.A. Statistica 6. Statisticheskij analiz danny’x: Uchebnik. M.: OOO «Binom-Press». 2007. 512 s.

[4] Littl R.Dzh.A., Rubin D.B. Statisticheskij analiz danny’x s propuskami: Per. s angl. M.: Finansy’ i statistika.1990. 336 s.

[5] Vyatchenin D.A., Nechetkie metody’ avtomaticheskoj klassifikaczii. Minsk: UP «Texnoprint». 2004. 219 s.

[6] Busse M., Orbanz P., Buhmann M. Cluster Analysis of Heterogeneous Rank Data. URL = http://www.machinelearning.org/proceedings /icml2007/ papers/341.pdf (ot 24.02.17).

[7] Masal’skix A.V. Parallel’ny’j algoritm odnogo metoda vosstanovleniya tablichny’x danny’x // Izvestiya Tul’skogo gosudarstvennogo universiteta. Estestvenny’e nauki. 2014. № 3. S. 67−77.

[8] Rahman M.M., Davis D.N. Fuzzy Unordered Rules Induction Algorithm Used as Missing Value Imputation Methods for K-Mean Clustering on Real Cardiovascular Data. URL = http://www2.dcs.hull.ac.uk/NEAT/dnd/papers/ Fuzzy_InductionAlgorithm_MVI.pdf (ot 4.05.18).

[9] Sibirev I.V., Afanas’eva T.V. Algoritm predobrabotki i vosstanovleniya anketny’x danny’x // Materialy’ VI Mezhdunar. nauch.-texn. konf. «Otkry’ty’e semanticheskie texnologii proektirovaniya intellektual’ny’x sistem» (Open Semantic Technologies for Intelligent Systems, OSTIS-2016). Minsk: BGUIR. 2016. S. 271−274.

[10] Sibirev I.V. Predobrabotka danny’x v intellektual’nom analize na osnove vosstanovleniya propushhenny’x anketny’x znachenij // Trudy’ Pyatnadczatoj naczional’noj konf. po iskusstvennomu intellektu s Mezhdunar. uchastiem KII-2016. V 3-x tomax. Smolensk: Universum. 2016. T. 1. S. 378−386.

[11] Sibirev I.V., Afanas’eva T.V. Analiz e’ffektivnosti algoritma vosstanovleniya anketny’x danny’x // Sb. nauchny’x trudov III Mezhdunar. nauchno-prakticheskaya konf. «E’lektronnoe obuchenie v neprery’vnom obrazovanii (E’ONO-2016)». Ul’yanovsk: UlGTU. 2016. 367−373 s.

[12] Sibirev I.V. Programma generator isxodny’x danny’x dlya klasterizaczii / Nechetkie sistemy’ i myagkie vy’chisleniya. Promy’shlenny’e primeneniya // Sb. nauchny’x trudov IV Vseros. nauchno- prakticheskoj mul’tikonferenczii s Mezhdunar. uchastiem «Prikladny’e informaczionny’e sistemy’ (PIS-2017)». Ul’yanovsk (Rossiya). 29−31 maya 2017. Ul’yanovsk: UlGTU. 2017. S. 171−174.

Abstract

How to cite

Related articles