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- W4386952746 abstract "Amount of experiment-related data in almost every domain has increased multifold. Hence, Understanding these novel datasets’ essential characteristics is vital to developing complex statistical models and testing various hypotheses. Exploratory data analysis using unsupervised techniques such as data clustering is becoming popular among researchers. Although several clustering algorithms exist, little research has been done to develop strategies to help understand the characteristics and physical implications of generated clusters. This paper proposes to use natural language to describe the results generated by a clustering algorithm linguistically for straightforward interpretation and understanding. We discuss salient characteristics of clusters that one looks for while interpreting clustering results and develop several linguistic prototype summaries for each of them. These summaries are then coherently combined to form linguistic summarization of clustering results. Numerical experiments on real-life IRIS dataset generate highly readable and easy-to-understand descriptions of the clusters generated through three popular clustering algorithms - k means, single linkage, and DBSCAN." @default.
- W4386952746 created "2023-09-23" @default.
- W4386952746 creator A5017193457 @default.
- W4386952746 creator A5071256774 @default.
- W4386952746 date "2023-07-19" @default.
- W4386952746 modified "2023-09-27" @default.
- W4386952746 title "Summarizing Clustering Results using Sentence prototype based Language Models" @default.
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- W4386952746 doi "https://doi.org/10.1109/iceccme57830.2023.10252800" @default.
- W4386952746 hasPublicationYear "2023" @default.
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