1. Let Vα be an evaluating view, eVα 2. Give a similarity function sim: S×S
α  [0,1], describing similar degree of each e k sim element k in S by the view of Vα, denoted by 3. Give an offset function *off: S×S*
α  , describing offset of each element k in S by R0 e off k the view of Vα, denoted by e k sim e off k 4. Define different weighting we and RDDk by and 5. For any element e in Vα calculate we 6. Calculate RDDk Now we give solution to detect outliers for RDD series data based on Definition 1, more content can also refer to the paper (Hsiao, 2009B), where a common center-safe algorithm was presented. The algorithm is the basis method for non-model data. 

IIR Algorithm ( the minimum is the safest)
Input: data set RDD with length N Output: outliers of RDD
1. Using rkto denote RDDk, we have {r0, r1, …, rN-1}
2. Sorting it in ascending order, we get ordered RDD set R{r0:N, r1:N, …,rN-1:N} r0:N ≤r1:N ≤ … ≤r*N-1:N*
3. Let Δi = (ri:N - ri-1:N) / (rN-1:N - r0:N) i ≥1 4. Define three quantities Expansion ratio: Eri = Δi * (N-1)
/( max( )) i i i j Ihr     
Inhibitory rate: j ≤ i Integrated inconsistent rate: IIRi = Eri/ Ihri 5. Let t = min(i) in all i that meet IIRi > c and i > (N-1) / 2 (c is an adjustable threshold, here we give 1.81)
6. Output k when rk ≥ rt:N 
 We combine the two algorithms together as a whole one, named as פ algorithm. The combination is seamless and very robust.