Comparing of results and implemantation of clustering methods of data mining software with a data set
İrem Düzdar Argun, Günay Temür · 2017
In data mining, the process of grouping similar data among each other in an evaluation made by the same kind of data is called clustering. Clusters are usually computed by algorithms that take advantage of relational or remote neighborhood relations to each other. A large number of clustering methods are used in practice. In this study, the differences between open source software used in data mining and results obtained by clustering methods of Oracle, Knime, Weka and RapidMiner programs are tabulated. As a source for this study, clustering analysis based article presentations by M. Turanlı, Ü.H.Özden and S.Türedi have been handled. For the evaluation, in 2006, economic similarities of 29 countries from candidate and member countries from the EU in terms of proprtion of inflation, Gross National Product, Internet Usage, Unemployment, Lifetime Education Indicators and Imports and Exports were tried to be clustered. Clustering operations were performed separately as 2-3-4 clusters in each program. In the clustering process, the euclidean distance is set for calculation and the number of iterations is 99 in each program. The obtained results were written in tabular form and the results were interpreted.