Compute the hub weights of a collection of transactions using the HITS (hubs and authorities) algorithm.
1 2 
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 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.
Class
transactions
,
function
weclat
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  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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