Abstract
The aim of this work is to propose a methodology for classifying, analyzing and visualizing data of patients with different symptoms from gynecological database. The application implements a variant of WITT algorithm for conceptual clustering. Pre-clustering algorithm is proposed that includes a tradeoff between overlapping of the initial clusters and displacing the center of clusters far away from the region of great density. To overcome the problem with weak correlation different coding schemes for cases are tested. Successful approach was to take square root of attribute value intervals to achieve the intervals with different sizes. Two different datasets from gynecological database are used: data related to polycystic ovary syndrome and data relevant to diagnose pre-eclampsia.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fisher, D.H.: Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning 2, 139–172 (1987)
Hanson, S.J., Bauer, M.: Conceptual Clustering, Categorization and Polymorphy. Machine Learning 3, 343–372 (1989)
Michalski, R.S.: Knowledge Acquisition through Conceptual Clustering: A theoretical Framework and Algorithm for Partitioning Data into Conjunctive Concepts. International Journal of Policy Analysis and Information Systems 4, 219–243 (1980)
Martinovska, C., Talmon, J.L.: Implementation of the alternative of the WITT algorithm. Techical report, University of Limburg, Faculty of Medical Informatics (1993)
Talmon, J.L., Braspenning, P.J., Fonteijn, H.: An analysis of the WITT algorithm. Machine Learning 11, 91–104 (1993)
Cekovska, S.: Biochemical Evaluation of the Functional Proteinuria. Ph.D. thesis. Faculty of Pharmacy – Skopje, Macedonia, pp. 87–94 (2006) (in Macedonian language)
Georgievska, J.: Influence of Insulin and Body Mass Index on the Endocrine and Metabolic Changes in Patients with Polycystic Ovary Syndrome. M.Sc. thesis. Faculty of Medicine. Clinic of Gynecology and Obstetrics (2006) (in Macedonian language)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martinovska, C. (2010). Conceptual Clustering and Analysis of Data from Gynecological Database. In: Davcev, D., Gómez, J.M. (eds) ICT Innovations 2009. ICT Innovations 2009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10781-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-10781-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10780-1
Online ISBN: 978-3-642-10781-8
eBook Packages: EngineeringEngineering (R0)