New Descriptors of Textual Records: Getting Help from Frequent Itemsets
| Autor: | Nadia Ghazzali, Ayoub Bokhabrine, Ismaïl Biskri |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
020203 distributed computing
Information retrieval lcsh:T58.5-58.64 k-medoids lcsh:Information technology Process (engineering) Computer science Social activity 02 engineering and technology ascending hierarchical clustering lcsh:QA75.5-76.95 frequent itemsets 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science |
| Zdroj: | Vietnam Journal of Computer Science, Vol 7, Iss 4, Pp 355-372 (2020) |
| ISSN: | 2196-8896 2196-8888 |
| DOI: | 10.1142/s2196888820500207 |
| Popis: | The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. The process of extraction of association rules is based on the lexical quality of the text and on the minimum support set by the user. In this paper, we implemented a platform named “IDETEX” capable of extracting itemsets from textual data and using it for the experimentation in different types of clustering methods, such as [Formula: see text]-Medoids and Hierarchical clustering. The experiments conducted demonstrate the potential of the proposed approach for defining similarity between segments. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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