Computing Transaction Weights With HITS
Compute the hub weights of a collection of transactions using the HITS (hubs and authorities) algorithm.
hits(data, iter = 16L, tol = NULL,
type = c("normed", "relative", "absolute"), verbose = FALSE)data |
an object of or coercible to class
|
iter |
an integer value specifying the maximum number of iterations to use. |
tol |
convergence tolerance (default |
type |
a string value specifying the norming of the hub weights.
For |
verbose |
a logical specifying if progress and runtime information should be displayed. |
Model a collection of transactions as a bipartite graph of hubs
(transactions) and authorities (items) with unit arcs and free
node weights. That is, a transaction weight is the sum of the
(normalized) weights of the items and vice versa. The weights
are estimated by iterating the model to a steady-state using
a builtin convergence tolerance of FLT_EPSILON for
(the change in) the norm of the vector of authorities.
A numeric vector with transaction weights for data.
Christian Buchta
K. Sun and F. Bai (2008). Mining Weighted Association Rules without Preassigned Weights. IEEE Transactions on Knowledge and Data Engineering, 4 (30), 489–495.
Class
transactions,
function
weclat
data(SunBai) ## calculate transaction weigths w <- hits(SunBai) w ## add transaction weight to the dataset transactionInfo(SunBai)[["weight"]] <- w transactionInfo(SunBai) ## calulate regular item frequencies itemFrequency(SunBai, weighted = FALSE) ## calulate weighted item frequencies itemFrequency(SunBai, weighted = TRUE)
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