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Clustering Categorical Data
Published in Charu C. Aggarwal, Chandan K. Reddy, Data Clustering, 2018
COBWEB creates a hierarchical clustering in the form of a classification tree. COBWEB incrementally organizes objects into a classification tree. A classification tree differs from decision trees, which label branches rather than nodes and use logical rather than probabilistic descriptions. Sibling nodes at a classification tree level form a partition [31].
Detection of interferences in an additive manufacturing process: an experimental study integrating methods of feature selection and machine learning
Published in International Journal of Production Research, 2020
Darko Stanisavljevic, David Cemernek, Heimo Gursch, Günter Urak, Gernot Lechner
The choice of Clustering algorithms was limited to these where the number of clusters can be specified, thereby fixing two clusters. Hence, the applied Clustering approaches split the provided data into two clusters, one including interferences and one without interferences. Please note that this approach is only valid under the conditions that labelled data exists, and data consists of exactly two different classes. Four clustering algorithms were selected from Weka, and for all of them the number of clusters was fixed at a value of two in order to allow an easy comparison with the results of the classification. Due to the runtime complexity only two of the four algorithms implemented in the pipeline were used in the evaluation. SimpleKMeans:k-means clustering.EM:Simple Expectation Maximisation clustering.HierarchicalClusterer:Hierarchical clustering (included in the pipeline but not used due to its runtime complexity).Cobweb:Incremental Hierarchical Conceptual clustering (included in the pipeline but not used due to its runtime complexity).