Abstract:
Outliers are the set of objects that are
considerably dissimilar from other values in a
random sample from a population. Outliers are
important because they can change the results of
data analysis. In this paper, two-phase clustering
approach is used to fulfil the detection of
abnormal diabetic and non-diabetic patients. The
patients’ data is taking from one of datasets
donated to UCI machine learning repository,
Pima Indians diabetes dataset from National
Institute of Diabetes and Digestive and Kidney
Diseases. In the phase-1, patients’ data are
clustered according to their similar features. In
the phase-2, a minimum spanning tree (MST) is
constructed to detect clusters with outliers. The
conclusion remarks on resulted abnormal patients
are given by the expert researcher of diabetes
disease and analyses on attributes' features are
also presented in this paper. Thus, it is intended to
detect abnormal patients of positive or negative in
diabetes test result and to illustrate analyses on
them.
