▁M . ▁Bre un ig , ▁Hans - Peter ▁Krie gel ▁and ▁J ör g ▁S ander . ▁Its ▁basic ▁idea ▁is ▁similar ▁to ▁DB SC AN , ▁but ▁it ▁addresses ▁one ▁of ▁DB SC AN ' s ▁major ▁weak ness es : ▁the ▁problem ▁of ▁detect ing ▁meaning ful ▁clusters ▁in ▁data ▁of ▁varying ▁density . ▁To ▁do ▁so , ▁the ▁points ▁of ▁the ▁database ▁are ▁( linear ly ) ▁ordered ▁such ▁that ▁spatial ly ▁closest ▁points ▁become ▁neighb ors ▁in ▁the ▁ordering . ▁Additionally , ▁a ▁special ▁distance ▁is ▁stored ▁for ▁each ▁point ▁that ▁represents ▁the ▁density ▁that ▁must ▁be ▁accepted ▁for ▁a ▁cluster ▁so ▁that ▁both ▁points ▁belong ▁to ▁the ▁same ▁cluster . ▁This ▁is ▁represented ▁as ▁a ▁d end ro gram . ▁ ▁Basic ▁idea ▁Like ▁DB SC AN , ▁OP T IC S ▁requires ▁two ▁parameters : ▁, ▁which ▁describes ▁the ▁maximum ▁distance ▁( radius ) ▁to ▁consider , ▁and ▁, ▁describing ▁the ▁number ▁of ▁points ▁required ▁to ▁form ▁a ▁cluster . ▁A ▁point ▁ ▁is ▁a ▁core ▁point ▁if ▁at ▁least ▁ ▁points ▁are ▁found ▁within ▁its ▁- ne igh bor hood ▁ ▁( including ▁point ▁ ▁itself ). ▁In ▁contrast ▁to ▁DB SC AN , ▁OP T IC S ▁also ▁cons iders ▁points ▁that ▁are ▁part ▁of ▁a ▁more ▁dens ely ▁pack ed ▁cluster , ▁so ▁each ▁point ▁is ▁assigned ▁a ▁core ▁distance ▁that ▁describes ▁the ▁distance ▁to ▁the ▁th ▁closest ▁point : ▁ ▁The ▁reach ability - distance ▁of ▁another ▁point ▁ ▁from ▁a ▁point