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 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.
Keywords
Metadata
Show full item recordCollections
- Pro gradu -tutkielmat [28143]
Related items
Showing items with similar title or keywords.
-
Knowledge of Nutrition and Physical Activity Guidelines is Not Associated with Physical Function in Dutch Older Adults Attending a Healthy Ageing Public Engagement Event
Ramsey, Keenan A.; Yeung, Suey S.Y.; Rojer, Anna G.M.; Gensous, Noémie; Asamane, Evans A.; Aunger, Justin Avery; Bondarev, Dmitriy; Cabbia, Andrea; Doody, Paul; Iadarola, Barbara; Rodrigues, Belina; Tahir, Muhammad R.; Kallen, Victor; Pazienza, Paola; Correia Santos, Nadine; Sipilä, Sarianna; Thompson, Janice L.; Meskers, Carel G.M.; Trappenburg, Marijke C.; Whittaker, Anna C.; Maier, Andrea B. (Dove Medical Press, 2022)Purpose: Evidence-based guidelines on nutrition and physical activity are used to increase knowledge in order to promote a healthy lifestyle. However, actual knowledge of guidelines is limited and whether it is associated ... -
Automatic knowledge discovery from sparse and large-scale educational data : case Finland
Saarela, Mirka (University of Jyväskylä, 2017)The Finnish educational system has received a lot of attention during the 21st century. Especially, the outstanding results in the first three cycles of the Programme for International Student Assessment (PISA) have made ... -
Intrusion detection applications using knowledge discovery and data mining
Juvonen, Antti (University of Jyväskylä, 2014) -
Knowledge discovery using diffusion maps
Sipola, Tuomo (University of Jyväskylä, 2013) -
Application of a Knowledge Discovery Process to Study Instances of Capacitated Vehicle Routing Problems
Kärkkäinen, Tommi; Rasku, Jussi (Springer, 2020)Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different ...