A Conceptual Hierarchy-Based Approach for the Extraction of Medical Knowledge in Psychiatric Diseases


Data mining is the method of analyzing data from various perspectives and summarizing it into useful information. Clustering is one of the prominent and efficient ways to use it as the data mining technique. Most of the clustering algorithms will usually employ distance metric based similarity measure to find the clusters such that the data points in the same cluster are similar; usually these algorithms use only discrete- valued databases. Instead this paper presents a conceptual clustering algorithm which can be employed on the categorical attributes as well as Boolean attributes. The use of the distance metric based similarity measure is not an appropriate method to be employed on the categorical attributes. So we proposed a new approach called HAC (Hierarchy of Concepts and Attributes), which can be used to measure the similarity between a set of data points. For a database table with the categorical attributes, our findings indicate that this HAC method will not only generates good quality clusters but also exhibits good scalability properties and it also organizes the data so as to maximize the inference capability[3].

  • Abstract
  • 1. Introduction
  • 2. Related work
  • 3. Hierarchy of Attributes and Concepts
  • 4. Implementation
  • 5. Results
  • 6. Conclusion and Future Directions
  • 7. Acknowledgements
  • 8. References

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