# hits

0th

Percentile

##### 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.

Class transactions, function weclat

• hits
##### 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 1.5-4, License: GPL-3

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