R: FP-Growth algorithm for association rule mining

hanaml.FPGrowth {hana.ml.r}

R Documentation

FP-Growth algorithm for association rule mining

Description

FP-Growth algorithm for association rule minining, based on PAL_FPGROWTH and PAL_FPGROWTH_RELATIONAL.

Usage

hanaml.FPGrowth(conn.context, data, used.cols = NULL,
               min.support = NULL, min.confidence = NULL,
               min.lift = NULL,
               relational = FALSE, max.item.length = NULL,
               max.consequent = NULL, ubiquitous = NULL,
               lhs.restrict = NULL, rhs.complement.lhs = NULL,
               rhs.restrict = NULL, lhs.complement.rhs = NULL,
               timeout = NULL, thread.ratio = NULL)

Arguments

`conn.context`	`ConnectionContext` Database connection object.
`data`	`DataFrame` Dataset used for association rule mininig.
`min.support`	`numeric` User-specified minimum support value for rule generation.
`min.confidence`	`numeric, optional` User-specified minimum confidence value for rule generation.
`used.cols`	`list of characters, optional` Specified the columns in `data` that specify transaction IDs and item IDs. For example, consider that the transaction ID column for `data` is "CUSTOMER", while the item ID colum for `data` is "ITEM", then the correct way to set up this parameter is used.cols = list("transaction" = "CUSTOMER", "item" = "ITEM"). Transaction ID column defaults to the 1st column of `data`, while item ID column defauts to the 2nd column of `data`.
`relational`	`logical, optional` Whether or not to apply relational logic for association rule mining. This will affect the format view of mined association rules. Defaults to FALSE.
`min.lift`	`numeric, optional` User-specified minimum lift value for rule generation. Defaults to 0.
`max.item.length`	`integer, optional` User-specified maximum length of items, inclusive of both antecedent and consequent items for association rule generation. Defaults to 5.
`max.consequent`	`double, optional` Maximum length of consequent items for association rule generation. Defaults to 100.
`ubiquitous`	`double, optional` User-specified maximum support value during the frequent items mining phase, i.e. if an item has support value above ubiquitous, it shall be ignored. Defaults to 1.0.
`lhs.restrict`	`list of characters, optional` Specifies the items are only allowed to be antecedent items, i.e. they can only appear on the left-hand side of association rules.
`rhs.complement.lhs`	`logical, optional` If 'lhs.restrict' is not NULL, you can set this paramter to TRUE to restrict rest of items so that they can only appear on the right-hand-side of association rules.
`rhs.restrict`	`list of characters, optional` Specifies the items are only allowed to be consequent items, i.e. they can only appear on the right-hand-side of association rules.
`lhs.complement.rhs`	`logical, optional` If 'rhs.restrict' is not NULL, you can set this paramter to TRUE to restrict rest of items so that they can only appear on the left-hand-side of association rules.
`timeout`	`integer, optional` Specifies the maximum run time in seconds for association rule mining. The algorithm will stop running when the specified timeout is reached.
`thread.ratio`	`double, optional` Controls the proportion of available threads to use. The value range is from 0 to 1, where 0 means only using 1 thread, and 1 means using at most all the currently available threads. Values outside this range tell PAL to heuristically determine the number of threads to use.

Format

R6Class object.

Value

An "FPGrowth" object with the following attributes:

result: DataFrame
Mined association rules as a whole. Each rule has its antecedent/consequent items and support/confidence/lift values. Available only when 'relatiional' is FALSE.
antecedent: DataFrame
Antecedent item information of mined association rules. Available only when relational is TRUE.
consequent: DataFrame
Consequent item information of mined association rules. Available only when relational is TRUE.
statistics: DataFrame
Support/confidence/lift values of mined association rules. Available only when relational is TRUE.
model: DataFrame
Mined associtaional rules in PMML format. Available only when pmml.export is 'single-row' or 'multi-row'.

Examples

## Not run: 
> df
   TRANS ITEM
1      1    1
2      1    2
3      2    2
4      2    3
5      2    4
6      3    1
7      3    3
8      3    4
9      3    5
10     4    1
11     4    4
12     4    5
13     5    1
14     5    2
15     6    1
16     6    2
17     6    3
18     6    4
19     7    1
20     8    1
21     8    2
22     8    3
23     9    1
24     9    2
25     9    3
26    10    2
27    10    3
28    10    5

> fpg <- hanaml.FPGrowth(conn.context = conn, data = df,
                        used.cols = c("transaction" = "TRANS", "item" = "ITEM"),
                        min.support = 0.2, min.confidence = 0.5,
                        max.item.length = 5, min.lift = 1,
                        max.consequent = 1, lhs.restrict = c(1,2,3),
                        timeout = 60)

> fpg$result
ANTECEDENT CONSEQUENT SUPPORT CONFIDENCE     LIFT
1          2          3     0.5  0.7142857 1.190476
2          3          2     0.5  0.8333333 1.190476
3          3          4     0.3  0.5000000 1.250000
4        1&3          4     0.2  0.5000000 1.250000
5        1&2          3     0.3  0.6000000 1.000000
6        1&3          2     0.3  0.7500000 1.071429

## End(Not run)

[Package hana.ml.r version 1.0.8 Index]