Mali, Kalyani ; Mitra, Sushmita (2002) Clustering of Symbolic Data and Its Validation In: Advances in Soft Computing — AFSS 2002, 29 January 2002, Springer Nature.
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Official URL: https://doi.org/10.1007/3-540-45631-7_45
Related URL: http://dx.doi.org/10.1007/3-540-45631-7_45
Abstract
Categorical clustering of symbolic data and its validation has been studied. Symbolic objects include linguistic, nominal, boolean, and interval-type data. Clustering in this domain involves the use of symbolic similarity and dissimilarity between the objects. The optimal number of meaningful clusters are determined in the process. The effectiveness of the symbolic clustering is demonstrated on a real life benchmark dataset.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to Springer Nature. |
| Keywords: | Categorical clustering; Data mining; Validation; Symbolic processing. |
| ID Code: | 140177 |
| Deposited On: | 07 Sep 2025 07:03 |
| Last Modified: | 07 Sep 2025 07:03 |
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