Empīriskās ticamības funkcijas izmantošana klasteru analīzē

6533b85ffe1ef96bd12c1591

RESEARCH PRODUCT

Empīriskās ticamības funkcijas izmantošana klasteru analīzē

subject

klasteru analīze hierarhiskie algoritmi uz modeļa balstīti Gausa jauktie modeļi Matemātika empīriskā ticamības funkcija

description

Bakalaura darbā apskatīta klasteru analīze ar empīriskās ticamības funkciju. Tās rezultāti salīdzināti ar pazīstamāko klasteru analīžu rezultātiem: hierarhisko klasterizāciju, k-vidējo klasterizāciju un uz modeļa balstīto Gausa jaukto modeļu klasterizāciju. Tika aplūkotas klasterizācijas metodes simulētiem un reāliem datu piemēriem, lai novērtētu, kurš algoritms veiksmīgāk veic datu klasterizāciju. Simulēto datu gadījumā Rand indekss rezultātus salīdzina ar sākotnējo datu grupām. Darba mērķis ir noskaidrot, kādas priekšrocības ir klasterizējot datus ar empīriskās ticamības funkciju. Gūtie rezultāti liecina, ka novērojumiem, kuros ir izlēcēji, klasteru analīze ar empīrisko ticamības funkciju veiksmīgi veic datu klasterizēšanu.

year	journal	country	edition	language
2019-01-01

https://dspace.lu.lv/dspace/handle/7/47455

PEPhysical Sciences and Engineering
- PE2Fundamental Constituents of Matter
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  - PE2_2 Phenomenology of fundamental interactions
  - PE2_3Experimental particle physics with accelerators
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  - PE2_6Nuclear, hadron and heavy ion physics
  - PE2_8Gas and plasma physics
  - PE2_5Classical and quantum physics of gravitational interactions
  - PE2_7Nuclear and particle astrophysics
  - PE2_9 Electromagnetism
  - PE2_10Atomic, molecular physics
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  - PE2_18Equilibrium and non-equilibrium statistical mechanics: steady states and dynamics
  - PE2_14Lasers, ultra-short lasers and laser physics
  - PE2_17Metrology and measurement
  - PE2_16Non-linear physics
  - PE2_12Optics, non-linear optics and nano-optics
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  - PE2_15Thermodynamics
- PE1Mathematics
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  - PE1_11Theoretical aspects of partial differential equations
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  - LS5_18Innovative methods and tools for neuroscience
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SHSocial Sciences and Humanities
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