Unsupervised Learning Method Based on Partitioning in Data Mining
Is this your thesis?
This record came from a bulk archive import. If it’s yours, link it to your profile.
Abstract (EN)
This study provides the introduction of some basic definitions about clustering method of data mining. For this purpose, it is given the methods of data mining, some algorithms of clustering method. Meanwhile, the k -Means clustering and Hierarchical clustering algorithms are defined. The aim of this study is to cluster the dataset into two clusters using Hierarchical clustering algorithm and k -Means algorithm. In order to achieve our target, two distance formulas are used to measure the distance between the vectors in the algorithms: the Euclidean distance and k -Nearest neighborhood distance.to compare two methods. Keywords: Data mining, data mining algorithms, data mining applications
Author
Kelechi Churchill Onyejiaka
How to Cite
Kelechi Churchill Onyejiaka (Master Thesis). Unsupervised Learning Method Based on Partitioning in Data Mining, 2015, Eastern Mediterranean University, Department of Mathematics.
Keywords
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from Eastern Mediterranean University
- Predicting performance level of reinforced concrete structures subject to corrosion as a function of time(2012)
- An Investigation on Time and Cost Overrun in Construction Projects(2012)
- Radial Power-Law Position-dependent Mass, Cylindrical Coordinates, Spectral Signatures(2015)
- Approaching a Successful Interior Design Atmosphere for Retail Clothing Stores Case of Dereboyu Street, Lefkoşa(2017)
- The Use of Passive Cooling Strategies to Improve the Thermal Comfort of Mid-rise Contemporary Residential Buildings in Abuja, Nigeria(2023)
- Community Based Tourism (CBT) Planning- A Mechanism for Socio-Ecology Survival: A Case Study of Lake Urmia IRAN(2018)
