# hits

##### Computing Transaction Weights With HITS

Compute the hub weights of a collection of transactions using the HITS (hubs and authorities) algorithm.

- Keywords
- models

##### Usage

```
hits(data, iter = 16L, tol = NULL,
type = c("normed", "relative", "absolute"), verbose = FALSE)
```

##### Arguments

- 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

`FLT_EPSILON`

).- type
a string value specifying the norming of the hub weights. For

`"normed"`

scale the weights to unit length (L2 norm), and for`"relative"`

to unit sum.- verbose
a logical specifying if progress and runtime information should be displayed.

##### Details

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.

##### Value

A `numeric`

vector with transaction weights for `data`

.

##### References

K. Sun and F. Bai (2008). Mining Weighted Association Rules
without Preassigned Weights. *IEEE Transactions on Knowledge
and Data Engineering*, 4 (30), 489--495.

##### See Also

Class
`transactions`

,
function
`weclat`

##### Examples

```
# NOT RUN {
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)
# }
```

*Documentation reproduced from package arules, version 1.5-5, License: GPL-3*