Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INSs are identified as central compact objects and X-ray dim isolated neutron stars that are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analysing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature, and transverse velocity of all discovered clusters. We verify that DPGMM. is robust and provide hints for inferring relations between different classes of pulsars, We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.