SC AN ▁clust ering ▁result ▁with ▁the ▁same ▁ ▁and ▁ ▁parameters ; ▁here ▁a ▁value ▁of ▁ 0 . 1 ▁may ▁yield ▁good ▁results ), ▁or ▁by ▁different ▁algorithms ▁that ▁try ▁to ▁detect ▁the ▁valle ys ▁by ▁ste ep ness , ▁kne e ▁detection , ▁or ▁local ▁maxim a . ▁Cl uster ings ▁obtained ▁this ▁way ▁usually ▁are ▁hier arch ical , ▁and ▁cannot ▁be ▁achieved ▁by ▁a ▁single ▁DB SC AN ▁run . ▁ ▁Complex ity ▁Like ▁DB SC AN , ▁OP T IC S ▁processes ▁each ▁point ▁once , ▁and ▁performs ▁one ▁- ne igh bor hood ▁query ▁during ▁this ▁processing . ▁Given ▁a ▁spatial ▁index ▁that ▁gr ants ▁a ▁neighborhood ▁query ▁in ▁ ▁runtime , ▁an ▁overall ▁runtime ▁of ▁ ▁is ▁obtained . ▁The ▁authors ▁of ▁the ▁original ▁OP T IC S ▁paper ▁report ▁an ▁actual ▁constant ▁slow down ▁factor ▁of ▁ 1 . 6 ▁compared ▁to ▁DB SC AN . ▁Note ▁that ▁the ▁value ▁of ▁ ▁might ▁heavily ▁influence ▁the ▁cost ▁of ▁the ▁algorithm , ▁since ▁a ▁value ▁too ▁large ▁might ▁raise ▁the ▁cost ▁of ▁a ▁neighborhood ▁query ▁to ▁linear ▁complexity . ▁ ▁In ▁particular , ▁choosing ▁ ▁( lar ger ▁than ▁the ▁maximum ▁distance ▁in ▁the ▁data ▁set ) ▁is ▁possible , ▁but ▁leads ▁to ▁quadratic ▁complexity , ▁since ▁every ▁neighborhood ▁query ▁returns ▁the ▁full ▁data ▁set . ▁Even ▁when ▁no ▁spatial ▁index ▁is ▁available , ▁this ▁comes ▁at ▁additional ▁cost ▁in ▁man aging ▁the ▁heap . ▁Therefore , ▁ ▁should ▁be ▁chosen ▁appropri ately ▁for ▁the