Semi-clustering That Scales: An Empirical Evaluation of GraphX

The development of Internet resulted in an increasing number of online text re-positories. In many cases, documents are assigned to more than one class and automatic multi-label classification needs to be used. When the number of labels exceeds the number of the documents, effective label space dimension reduction may signifi-cantly improve classification accuracy, what is a major priority in the medical field. In the paper, we propose document clustering for label selection. We use semi-clustering method, by considering graph representation, where documents are represented by vertices and edge weights are calculated according to their mutual similarity. Assigning documents to semi-clusters helps in reducing number of labels, further used in multilabel classification process. The performance of the method is examined by experiments conducted on real medical datasets.

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Semi-clustering That Scales: An Empirical Evaluation of GraphX

Cited by 3 publications

References 9 publications

Graph Representation and Semi-clustering Approach for Label Space Reduction in Multi-label Classification of Documents

Graph Representation and Semi-clustering Approach for Label Space Reduction in Multi-label Classification of Documents

A Comparative Evaluation of Big Data Frameworks for Graph Processing

Cluster Analysis of Medical Text Documents by Using Semi-Clustering Approach Based on Graph Representation

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