Knowledge discovery from physical activity

Tässä pro gradu -tutkielmassa käydään läpi Knowledge Discovery in Databases (KDD) -prosessi ja sen soveltamismahdollisuuksia fyysiseen aktiivisuuteen liittyvän datan kanssa. KDD-prosessi koostuu monesta eri vaiheesta, sisältäen esikäsittelyn, datan muunnoksen ja tiedonlouhinnan. Tässä tutkielmassa t...

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Main Author: Jauhiainen, Susanne
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, University of Jyväskylä, Jyväskylän yliopisto
Format: Master's thesis
Language:eng
Published: 2017
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/54175
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author Jauhiainen, Susanne
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia University of Jyväskylä Jyväskylän yliopisto
author_facet Jauhiainen, Susanne Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia University of Jyväskylä Jyväskylän yliopisto Jauhiainen, Susanne Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia University of Jyväskylä Jyväskylän yliopisto
author_sort Jauhiainen, Susanne
datasource_str_mv jyx
description Tässä pro gradu -tutkielmassa käydään läpi Knowledge Discovery in Databases (KDD) -prosessi ja sen soveltamismahdollisuuksia fyysiseen aktiivisuuteen liittyvän datan kanssa. KDD-prosessi koostuu monesta eri vaiheesta, sisältäen esikäsittelyn, datan muunnoksen ja tiedonlouhinnan. Tässä tutkielmassa tiedonlouhinnan menetelmänä käytetään klusterointia, joka käydään läpi yksityiskohtaisesti. Vertailemme myös laajan joukon eri klusterointi indeksejä (CVAIs) sekä niiden eri toteutuksia k-means klusteroinnin kanssa ja esittelemme parhaat näistä yleisemmässä muodossa. Tutkielman empiirisessä osassa seitsemäsluokkalaisten koululaisten aktiivisuusdataa tutkitaan KDD-prosessia seuraten ja hyödyntäen monia eri datan muunnoksia ja klusterointimenetelmiä. Tarkoituksena on selvittää, voiko ohjaamattoman tiedonlouhinnan avulla löytää uutta ja hyödyllistä informaatiota datasta. In this master’s thesis the Knowledge Discovery in Databases (KDD) process and its usage with physical activity data are discussed. The KDD process has multiple steps, including preprocessing, transformation, and data mining. Clustering is used as the data mining technique and is introduced in detail. A large set of different Cluster Validation Indices (CVAIs) and their implementations are tested with the k-means clustering and the best performing ones further generalized. In the empirical part, physical activity data from Finnish seventh-grade students is assessed following the KDD process and using multiple different transformations with different clustering methods. The aim is to find out, whether unsupervised data mining can help detect novel and useful information from this data.
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spellingShingle Jauhiainen, Susanne Knowledge discovery from physical activity knowledge discovery physical activity cluster validation index Tietotekniikka Mathematical Information Technology 602 fyysinen aktiivisuus klusterit tiedonlouhinta
title Knowledge discovery from physical activity
title_full Knowledge discovery from physical activity
title_fullStr Knowledge discovery from physical activity Knowledge discovery from physical activity
title_full_unstemmed Knowledge discovery from physical activity Knowledge discovery from physical activity
title_short Knowledge discovery from physical activity
title_sort knowledge discovery from physical activity
title_txtP Knowledge discovery from physical activity
topic knowledge discovery physical activity cluster validation index Tietotekniikka Mathematical Information Technology 602 fyysinen aktiivisuus klusterit tiedonlouhinta
topic_facet 602 Mathematical Information Technology Tietotekniikka cluster validation index fyysinen aktiivisuus klusterit knowledge discovery physical activity tiedonlouhinta
url https://jyx.jyu.fi/handle/123456789/54175 http://www.urn.fi/URN:NBN:fi:jyu-201705302561
work_keys_str_mv AT jauhiainensusanne knowledgediscoveryfromphysicalactivity