eclat                 package:arules                 R Documentation

_C_a_l_l _E_c_l_a_t

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

     Mine frequent itemsets with the Eclat algorithm. This algorithm
     uses simple intersection operations for equivalence class
     clustering along with bottom-up lattice traversal.

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

     eclat(data, parameter = NULL, control = NULL)

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

    data: object of class 'transactions' or any data structure which
          can be coerced into 'transactions' (e.g., binary 'matrix',
          'data.frame').

parameter: object of class 'ECparameter' or named list (default values
          are: support 0.1 and maxlen 5)

 control: Object of class 'ECcontrol' or named list for algorithmic
          controls.

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

     Calls the C implementation of the Eclat algorithm by Christian 
     Borgelt for mining frequent itemsets.

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

     Returns an object of class 'itemsets'.

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

     Bettina Gruen and Michael Hahsler

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

     Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, and
     Wei Li. _New algorithms for fast discovery of association rules_.
     Technical Report 651, Computer Science Department, University of
     Rochester, Rochester, NY 14627, July 1997.

     Christian Borgelt. Efficient Implementations of Apriori and Eclat.
     _Workshop of Frequent Item Set Mining Implementations_ (FIMI 2003,
     Melbourne, FL, USA).

_S_e_e _A_l_s_o:

     'ECparameter-class', 'ECcontrol-class', 'transactions-class',
     'itemsets-class', 'apriori'

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

     data("Adult_transactions")
     ## Mine itemsets with minimum support of 0.1.
     itemsets <- eclat(Adult_transactions,
                       parameter = list(supp = 0.1, maxlen = 15))

