PM₁₀ (particulate matter with aerodynamic diameter below 10 microns) has always caught scientific attention due to its effect to human health. Predicting PM₁₀ concentration is essential for early preventive measures, especially for cities such as Kota Kinabalu. Temporal data clustering may enhance accuracy of prediction model by group data in time range. However, the necessity of temporal data clustering has yet to be studied in Kota Kinabalu. OBJECTIVE. This research is conducted to compare significance of meteorological and pollutant factors for PM₁₀ variation in clustered and unclustered data. METHODOLOGY. This study is focused in Kota Kinabalu, Sabah. The data for meteorological factors (Ws, Wd, Hum, Temp) and pollutant factors (CO₂, NO₂, O₃, SO₂, PM₁₀) from 2003 to 2012 provided by Department of Environment are used for this research. Missing data are imputed using nearest neighbour method before it is clustered by monsoonal clustering. Unclustered and clustered datasets are analysed using principal component analysis (PCA) to check significance of factors contributing to PM₁₀ concentration. FINDINGS. PCA results show that temporal clustering does not have noticeable effect on the variation of PM₁₀ concentration. For all datasets, humidity and x-component wind speed have highest factor loading on PC₁ and PC₂ respectively. Further statistical analysis by 2-D regression shows that humidity (ρ = -0.60 ± 0.20) and temperature (ρ = 0.63 ± 0.11) have moderate to strong correlation towards PM₁₀ concentration. This may be due to high humidity level and strong negative correlation between temperature and humidity (ρ = -0.91 ± 0.03). In contrast, both x- and y-component wind speed generally show weak correlation towards PM₁₀, with ρ value of 0.09 ± 0.14 and 0.24 ± 0.18 respectively probably because of varying direction of particle dispersion. Fourier analysis further confirms this result by showing that human activity contributes major effect to variation of PM₁₀ concentration.