| Autor: |
Suh, Sang C., Pabbisetty, Nagendra B., Anaparthi, Sri G. |
| Rok vydání: |
2010 |
| Zdroj: |
Machine Learning |
| DOI: |
10.5772/9154 |
| Popis: |
In this paper, we studied the hierarchical conceptual clustering applied on structured databases. We have given a new method of HAC with conceptual clustering to explore categorical data. The main contributions of the paper are to develop HAC algorithm which is applied on categorical attributes instead of traditional algorithms which apply the distance metric measures. The results show that the data is categorized using hierarchical conceptual clustering with HAC. There are numerous types of clustering techniques most of these techniques are applicable only on the unstructured data. Sometimes, there is a need to apply clustering on categorical attributes which is not suitable to apply on it. So, the nonmetric measures are used to perform clustering on the categorical attributes which represents the closest proximity between the data attributes. HAC is used to represents databases in the form concept tables for categorical data which contains the concepts formed on the domain. This technique can be applied on any of the fields which have structure data. The information is extracted using the HAC algorithm from structured data. The structured data is applied on input and the results formed are rules extracted from the data. Clustering is important for both types of data. The modern data mining mechanisms are used to apply on the data. From a machine learning standpoint, this research has been greatly influenced by work in conceptual clustering. HAC seeks classifications that maximize a heuristic measure (as in conceptual clustering systems) and uses a search strategy abstracted from incremental systems such as UNIMEM [8]. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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