In this work we are presenting the extension of the FPCM using Type-2 Fuzzy Logic Techniques to provide this method with the capability of handling a higher degree of uncertainty in a dataset to solve real world problems where data clustering is involved.
Section 2 describes the extension of the FPCM algorithm presented in this paper, Section 3 shows the concept of cluster validation index to measure the performance of the clustering algorithm, Section 4 shows the results obtained by the IT2FPCM algorithm and its comparison with the IT2FCM algorithm, and Section 5 contains the conclusions and future work.
This is an extension of the FPCM algorithm proposed by N.
This extension on the FPCM algorithm is intended to show that this algorithm is capable of handling uncertainty and is less susceptible to noise.
Pearson's correlation coefficient was calculated to study correlation between the haemoglobin levels by FPCM method and HCS method.
The HCS method significantly overestimated the haemoglobin level when compared to FPCM method (P< 0.001) (Table I).
The prevalence of anaemia was 80.3 per cent by FPCM method (Hb < 110 g/l), while it was 72.4 and 54.0 per cent by HCS method (Hb < 110 g/l) and by pallor method respectively.