Apriori

hanaml.Apriori is a R wrapper for SAP HANA PAL APRIORI and PAL APRIORI_RELATIONAL.

hanaml.Apriori(
  data,
  used.cols = NULL,
  min.support,
  min.confidence,
  min.lift = NULL,
  relational = FALSE,
  max.item.length = NULL,
  max.consequent = NULL,
  use.prefix.tree = NULL,
  ubiquitous = NULL,
  lhs.restrict = NULL,
  rhs.complement.lhs = NULL,
  rhs.restrict = NULL,
  lhs.complement.rhs = NULL,
  timeout = NULL,
  thread.ratio = NULL,
  pmml.export = NULL
)

Arguments

data	DataFrame DataFrame containting the data.
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 column 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 defaults to the 2nd column of data.
min.support	numeric User-specified minimum support value for rule generation.
min.confidence	numeric User-specified minimum confidence value for rule generation.
min.lift	numeric, optional User-specified minimum lift value for rule generation. Defaults to 0.
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.
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.
use.prefix.tree	logical, optional Indicates whether or not to use prefix tree data structure when generating association rules for the purpose of memory-saving. Defaults to FALSe.
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. No default value.
rhs.complement.lhs	logical, optional If lhs.restrict is not NULL, you can set this parameter to TRUE to restrict rest of items so that they can only appear on the right-hand-side of association rules. Defaults to 0.
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. No default value.
lhs.complement.rhs	logical, optional If rhs.restrict is not NULL, you can set this parameter to TRUE to restrict rest of items so that they can only appear on the left-hand-side of association rules. Defaults to 0.
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. Defaults to 3600
thread.ratio	double, optional Controls the proportion of available threads that can be used by this function. The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates all available threads. Values between 0 and 1 will use up to that percentage of available threads.Values outside this range are ignored. Defaults to 0.
pmml.export	{"no", "single-row", "multi-row"}, optional Controls whether to output a PMML representation of the model, and how to format the PMML. "no": No PMML model. "single-row": Exports a PMML model in a maximum of one row. Fails if the model doesn't fit in one row. "multi-row": Exports a PMML model, splitting it across multiple rows if it doesn't fit in one. Default to "no".

Value

An "Apriori" 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 association rules in PMML format. Available only when pmml.export is 'single-row' or 'multi-row'.

Examples

Input DataFrame data:

> data$Collect()
   CUSTOMER  ITEM
1         2 item2
2         2 item3
3         3 item1
4         3 item2
5         3 item4
6         4 item1
7         4 item3
8         5 item2
9         5 item3
10        6 item1
11        6 item3
12        0 item1
13        0 item2
14        0 item5
15        1 item2
16        1 item4
17        7 item1
18        7 item2
19        7 item3
20        7 item5
21        8 item1
22        8 item2
23        8 item3

Call the function:

> apr <- hanaml.Apriori(data = data, min.support = 0.1, min.confidence = 0.3,
                        min.lift = 1.10, max.consequent = 1, pmml.export = "single-row")

Output:

> apr$result
          ANTECEDENT CONSEQUENT   SUPPORT CONFIDENCE     LIFT
1              item5      item2 0.2222222  1.0000000 1.285714
2              item1      item5 0.2222222  0.3333333 1.500000
3              item5      item1 0.2222222  1.0000000 1.500000
4              item4      item2 0.2222222  1.0000000 1.285714
5        item2&item1      item5 0.2222222  0.5000000 2.250000
6        item5&item1      item2 0.2222222  1.0000000 1.285714
7        item5&item2      item1 0.2222222  1.0000000 1.500000
8        item5&item3      item2 0.1111111  1.0000000 1.285714
9        item5&item3      item1 0.1111111  1.0000000 1.500000
10       item1&item4      item2 0.1111111  1.0000000 1.285714
11 item2&item1&item3      item5 0.1111111  0.5000000 2.250000
12 item5&item1&item3      item2 0.1111111  1.0000000 1.285714
13 item5&item2&item3      item1 0.1111111  1.0000000 1.500000