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Mahalanobis distance is a distance base on correlation structure between variable which have different shape that can be identified and analyzed. Mahalanobis distance usually used in cluster analyzation, outlier identification an another classification. Outlier identification method is still being a...

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Main Author: SUSILAWATI (NIM 10103039), EKA
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/10319
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:10319
spelling id-itb.:103192017-09-27T11:43:05Z#TITLE_ALTERNATIVE# SUSILAWATI (NIM 10103039), EKA Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/10319 Mahalanobis distance is a distance base on correlation structure between variable which have different shape that can be identified and analyzed. Mahalanobis distance usually used in cluster analyzation, outlier identification an another classification. Outlier identification method is still being a focus for efficiency improvement. In this final project, the author try to present it base on literature and simulation. Determinan methode have a same effctivity but have a better effecience than MVV (Minimum Vector Variance) method. In this research the author use Normal datas with many variation of size data. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Mahalanobis distance is a distance base on correlation structure between variable which have different shape that can be identified and analyzed. Mahalanobis distance usually used in cluster analyzation, outlier identification an another classification. Outlier identification method is still being a focus for efficiency improvement. In this final project, the author try to present it base on literature and simulation. Determinan methode have a same effctivity but have a better effecience than MVV (Minimum Vector Variance) method. In this research the author use Normal datas with many variation of size data.
format Final Project
author SUSILAWATI (NIM 10103039), EKA
spellingShingle SUSILAWATI (NIM 10103039), EKA
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author_facet SUSILAWATI (NIM 10103039), EKA
author_sort SUSILAWATI (NIM 10103039), EKA
title #TITLE_ALTERNATIVE#
title_short #TITLE_ALTERNATIVE#
title_full #TITLE_ALTERNATIVE#
title_fullStr #TITLE_ALTERNATIVE#
title_full_unstemmed #TITLE_ALTERNATIVE#
title_sort #title_alternative#
url https://digilib.itb.ac.id/gdl/view/10319
_version_ 1820664941610270720

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