• THOTA YAMINI KALYANI
• K.VENKATESH
• D.D.D.SURIBABU

The effectiveness of a particular algorithm may be influenced by a number of different factors. However, the influence of a particular factor like quality can be considered to identify the effectiveness of an algorithm for a given data source. The project provides a comparative analysis of two clustering algorithms namely K-Means, and MST applied to diabetes dataset. Besides rapidly generating the clusters, the analysis also provides a basis for determining the quality of the clusters generated and helps in identifying the algorithm that generates good quality clusters. As Data Characterization is a summarization of the general characteristics or features of a target class of data, the characteristics of diabetes data are also analyzed taking into account positively tested records as target class of data using the approach of Attribute Oriented Induction.

Characterization, Summarization, Records, K-Means, Minimum Spanning Tree

"DIABETES PREDICTION USING MULTIPLE CLASSIFICATION ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.g607-g614, July-2021, Available :http://www.jetir.org/papers/JETIR2107808.pdf

"DIABETES PREDICTION USING MULTIPLE CLASSIFICATION ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppg607-g614, July-2021, Available at : http://www.jetir.org/papers/JETIR2107808.pdf