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A combination of graph based and cell based clustering techniques for big datasets

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Nội dung chi tiết: A combination of graph based and cell based clustering techniques for big datasets

A combination of graph based and cell based clustering techniques for big datasets

A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

A combination of graph based and cell based clustering techniques for big datasets QUIREMENTSFOR THE DEGREE OF DOCTOR OF PHILOSOPHYIN INFORMATION TECHNOLOGYDEPARTMENT OF INFORMATION TECHNOLOGYGRADUATE COLLEGEKING MONGKUTS UNIVERSITY

OF TECHNOLOGY NORTH BANGKOKACADEMIC YEAR 2016COPYRIGHT OF KING MONGKUTS UNIVERSITY OF TECHNOLOGY NORTH BANGKOKName: Mr.Duong Van llicuIhesis l illc: A A combination of graph based and cell based clustering techniques for big datasets

Combmation of Graph-based and Cell-Based Clustering Techniques for Big DatasetsMajor Field: Information Technology (International Program) King Mongk

A combination of graph based and cell based clustering techniques for big datasets

ut’s University of Technology North BangkokThesis Advisor: Associate Professor Dr.Phayung MeesadAcademic Year : 2016AbstractBig dataset analysis is mo

A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

A combination of graph based and cell based clustering techniques for big datasets nologies and suitable algorithms. This study proposes a fast outlier detection algorithm for big datasets (Cell-RDOS) and two clustering algorithms fo

r big datasets on a limited memory computer (Cell-MST-based and Weighted Cell-MST-based). The Cell-RDOS algorithm is a combination of cell-based algor A combination of graph based and cell based clustering techniques for big datasets

ithms and a revised version of the rankingbased outlier detection algorithm with various depths (RDOS). The Cell-RDOS algorithm can produce the same r

A combination of graph based and cell based clustering techniques for big datasets

esults compared to the RDOS algorithm, and reduce up to 99% executing time of the RDOS algorithm when working with big datasets. The proposed clusteri

A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

A combination of graph based and cell based clustering techniques for big datasets orm many other algorithms in terms of less used memory, accuracy level, and speed, firstly. they can reduce up to 99% required memory size compared to

the previous methods such as Similarity-based. Decision-theoretic Rough Set and MS 1-based algorithms.(Total 101 pages)Keywords : Big dataset, cell-b A combination of graph based and cell based clustering techniques for big datasets

ased clustering, graph-based clustering, minimumspanning tree, outlier detection.Advisorii: uiuíniinuíh tfriiwiuvnil:niiHWMHaiuiwniiflnimnqiJijujiunnY

A combination of graph based and cell based clustering techniques for big datasets

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A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

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nanl) Cell-RDOS was 2) 4iJflOu5tni3fanqutfo»Jii 2 IB Cell-MST-based tins WeightedCell-MST-based dnnu^unouiBnuflUin-uojjnuonnq'ij Cell-RDOS iăwnnwiĩUH

A combination of graph based and cell based clustering techniques for big datasets

ffii4ĩgini4iiìníin cell-based tins RDOS HnmiiBUYiuii Cell-RDOS WHanirfuMifouauonnqummjmifhriii RDOSIIUUIÂM nwamaifniiJmnawaaiiN 99% lurmiiinunuva^oijn

A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

A combination of graph based and cell based clustering techniques for big datasets QD làanìTUuaouiBHUUẳíiAU lawn Similarity-based. Decision-theoretic Rough Set was MST-bascd ếiunnlíinhariìiuíìniiỡtiíìSiuuuiâỉiãi 99% antu^iufliiijQnAO

j lunmanqu wasfiiiwnlunisdnuiiiwtitHJweniAn'hmmjmAu(iiwiwiiwBMiiuiwiuau 101 Mill)fliriimy : ^^oiJO-uuiflliiiU miỉíanquuuỊiuinín miiflnquuujiurTJid iii A combination of graph based and cell based clustering techniques for big datasets

iihjniiiJu Ú 4 in fl ntfinintfo Hniwnnfj IJ_____________________________________________________0 n 1 ĩ ốíỉ n b lintnuii 14 í 11 aniiiACKNOWLEDGEMENTS

A combination of graph based and cell based clustering techniques for big datasets

1 am greatly pleased to express my sincere thanks to the people who made this thesis possible. Ĩ am enormously grateful to Associate Professor Dr.Phay

A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

A combination of graph based and cell based clustering techniques for big datasets is.1 w ould like to express my thanks to teachers who have taught me during my Doctor of Philosophy in Information Technology program. I am indebted t

o their knowledge, helpful suggestions and valuable assistance throughout my study in Thailand.Thanks to the Decision 911/QD-TTg signed on 17/06/2010 A combination of graph based and cell based clustering techniques for big datasets

by the Vietnamese Prime Minister (known as Project 911) and the Vietnam International Education Development Department (VIED) who supported scholarshi

A combination of graph based and cell based clustering techniques for big datasets

p during my three years of Doctor Degree study in Thailand. Without their support, it would have been impossible for me to have a chance to finish my

A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

A COMBINATION OF GRAPH-BASED AND CELL-BASED CLUSTERINGTECHNIQUES FOR BIG DATASETSMR.DUONG VAN HIEUA THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQ

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