lofactor                package:dprep                R Documentation

_L_o_c_a_l _O_u_t_l_i_e_r _F_a_c_t_o_r

_D_e_s_c_r_i_p_t_i_o_n:

     A function that finds the local outlier factor (Breunig et
     al.,2000) of the matrix "data" with k neighbors. The local outlier
     factor (LOF) is a measure of outlyingness that is calculated for
     each observation. The user decides whether or not an observation 
     will be considered an outlier or not based on this measure. The
     LOF takes into consideration the density of the neighborhood
     around the observation to determine its outlyingness.

_U_s_a_g_e:

     lofactor(data, k)

_A_r_g_u_m_e_n_t_s:

    data: the data set to be explored

       k: the value that determines the k-distance to be used to
          calculate the LOF's.

_D_e_t_a_i_l_s:

     The LOF are calculated over a range of values, and the max local
     outlier factor is determined over this range.

_V_a_l_u_e:

     lof: a vector with the local outlier factor of each observation

_A_u_t_h_o_r(_s):

     Caroline Rodriguez

_R_e_f_e_r_e_n_c_e_s:

     Breuning, M., Kriegel, H., Ng, R.T, and Sander. J. (2000).  LOF:
     Identifying density-based local outliers. In Proceedings of the
     ACM SIGMOD  International Conference on Management of Data.

_E_x_a_m_p_l_e_s:

     #---- Detecting the top 10 outliers using the LOF algorithm----
     data(my.iris)
     iris.lof=lofactor(my.iris,10)

