Optical and microphysical properties, for example, refractive index, single scattering albedo (SSA) and asymmetry factor were attained from AERONET ground based sun sky radiometer situated at rural coastal Erdemli in the Eastern Mediterranean between 2000 and 2014. On average, imaginary part of the refractive index (REFI similar to 0.007) and SSA (similar to 0.93) denoted that the aerosol composition was similar to that of Industrial/Urban aerosol type. However, the daily variation in optical and microphysical properties was greatly influenced by diverse aerosol types. REFI for relatively clean dust events decreased from similar to 0.010 at 440 nm to 0.002 at 1020 nm whereas corresponding SSA increased from similar to 0.80 at 440 nm to similar to 0.96 at 1020 nm. Comparatively, lower REFI and higher SSA values were observed when the dust particles mixed with secondary aerosols might be due to shielding of iron oxides by sulfate and nitrate particles. On the contrary, larger REFI (up to 0.044 at 440 nm) and lower SSA (down to 0.72 at 440 nm) values were found for dust mixed with smoke particles likely because of lensing effect. REFI for spring reduced from 0.065 at 440 nm to 0.038 at 1020 nm with SSA rising from 0.92 to 0.95 owing to frequent dust episodes take place in this period. Although, summer and fall exhibited similar wavelength dependence of REFI and SSA, the former denoted more absorbing character compared to the latter. This difference might be ascribed to anthropogenic aerosol composition, such as black carbon and secondary aerosol, with diverging contributions. Hierarchical cluster analyses revealed six classes namely, dust, dust mixed with smoke particles, smoke, smoke with secondary, secondary with smoke and secondary aerosol type. Relative contributions from hierarchical cluster analyses and angstrom exponent classification yielded coherent results, dust and anthropic being around 20% and 80%, respectively. Whereas, k-means exhibited overestimation and underestimation for dust (35%) and anthropogenic (65%) aerosol type, respectively. The hierarchical cluster analysis was more powerful tool to classify aerosol type than k-means, thus the application of the k-means should be performed with an utmost care.