hits {arules}  R Documentation 
Compute the hub transaction weights for 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 transactions. 
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
steadystate 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.
Other weighted association mining functions:
SunBai
,
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)