Title Details: | |
Clustering with the k-means Algorithm |
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Authors: |
Maravelakis, Petros |
Description: | |
Abstract: |
In this chapter we will present clustering using the k-means algorithm. First, we will introduce the concept of cluster analysis. Next, we will refer to the main features of the k-means algorithm and refer to the concept of distance. The next step is the process of applying the algorithm to RapidMiner. Two case studies will be presented in which there will be a detailed presentation of the problem to be studied, the data that will be used in the analysis, the application of the k-means method in RapidMiner, the results (tables and diagrams), as well as their annotation in order to extract the necessary conclusions.
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Linguistic Editors: |
Paxinou, Evgenia |
Graphic Editors: |
Skouloudis, George |
Type: |
Chapter |
Creation Date: | 01-05-2024 |
Item Details: | |
License: |
Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
Handle | http://hdl.handle.net/11419/13259 |
Bibliographic Reference: | Maravelakis, P. (2024). Clustering with the k-means Algorithm [Chapter]. In Maravelakis, P. 2024. Business Data Mining [Undergraduate textbook]. Kallipos, Open Academic Editions. https://hdl.handle.net/11419/13259 |
Language: |
Greek |
Is Part of: |
Business Data Mining |
Publication Origin: |
Kallipos, Open Academic Editions |