Comput Methods Programs Biomed - Fuzzy and hard clustering analysis for thyroid disease.

Tópicos

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Resumo

Thyroid hormones produced by the thyroid gland help regulation of the body's metabolism. A variety of methods have been proposed in the literature for thyroid disease classification. As far as we know, clustering techniques have not been used in thyroid diseases data set so far. This paper proposes a comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters. Different scalar validity measures are used in comparing the performances of the proposed clustering systems. To demonstrate the performance of each algorithm, the feature values that represent thyroid disease are used as input for the system. Several runs are carried out and recorded with a different number of clusters being specified for each run (between 2 and 11), so as to establish the optimum number of clusters. To find the optimal number of clusters, the so-called elbow criterion is applied. The experimental results revealed that for all algorithms, the elbow was located at c=3. The clustering results for all algorithms are then visualized by the Sammon mapping method to find a low-dimensional (normally 2D or 3D) representation of a set of points distributed in a high dimensional pattern space. At the end of this study, some recommendations are formulated to improve determining the actual number of clusters present in the data set.

Resumo Limpo

thyroid hormon produc thyroid gland help regul bodi metabol varieti method propos literatur thyroid diseas classif far know cluster techniqu use thyroid diseas data set far paper propos comparison hard fuzzi cluster algorithm thyroid diseas data set order find optim number cluster differ scalar valid measur use compar perform propos cluster system demonstr perform algorithm featur valu repres thyroid diseas use input system sever run carri record differ number cluster specifi run establish optimum number cluster find optim number cluster socal elbow criterion appli experiment result reveal algorithm elbow locat c cluster result algorithm visual sammon map method find lowdimension normal d d represent set point distribut high dimension pattern space end studi recommend formul improv determin actual number cluster present data set

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