A brief study on clustering methods: Based on the k-means algorithm

Clustering is the process of grouping a set of objects into classes. The clustering problem has been addressed by researchers in many contexts and disciplines. First, a process model for data mining and the typical requirements of clustering methods have been described. Second, the k-means algorithm...

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Bibliographic Details
Published in:2011 International Conference on E-Business and E-Government pp. 1 - 5
Main Authors: Peng, Chen, Guiqiong, Xu
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2011
Subjects:
Algorithm design and analysis
Classification algorithms
cluster algorithm
Clustering algorithms
Clustering methods
Complexity theory
Data mining
k-means
kdd
knime
ISBN:9781424486915, 1424486912
Online Access:Get full text
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Summary:Clustering is the process of grouping a set of objects into classes. The clustering problem has been addressed by researchers in many contexts and disciplines. First, a process model for data mining and the typical requirements of clustering methods have been described. Second, the k-means algorithm and its advantages and disadvantages are introduced. Then the Iris dataset is used to specify the k-means algorithm. A taxonomy of clustering algorithms and complexity of several algorithms are listed in the end.
ISBN:9781424486915
1424486912
DOI:10.1109/ICEBEG.2011.5881902