Association rules using titanic data

The association rules

This presentation is using to demonstrate the association rules used in the shiny app First introducing some basic conveps of the assocation rules.
(have rule like {X} -> {Y})

• surpport
  Fraction of transactions that contain an itemset.(P(XY))

• confidence
  Measures how often items in Y appear in transactions that contain X.(P(XY)/P(X))

• lift The ratio between confidence and the prior probability，A lift value less (larger) than 1 indicates a negative (positive) dependence or substitution (complementary) effect.(P(XY)/P(X)P(Y))

Apriori

• Generating the frequent itemsets.
  Frequent itemsets.(The support of the itemsets larger than the defined minimum support)
  
• Generating rules from the frequent itemsets.
  Calculating the confidence or lift of every frequent itemsets’ subsets. choose the rules according the measurement we defined.(minimum confidence or lift).

An example using Titanic raw data

##   Class  Sex   Age Survived
## 1   3rd Male Child       No
## 2   3rd Male Child       No
## 3   3rd Male Child       No
## 4   3rd Male Child       No
## 5   3rd Male Child       No
## 6   3rd Male Child       No

Generating all the rules

## Loading required package: Matrix

## 
## Attaching package: 'arules'

## The following objects are masked from 'package:base':
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##     abbreviate, write

## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.8    0.1    1 none FALSE            TRUE       5     0.1      1
##  maxlen target   ext
##      10  rules FALSE
## 
## Algorithmic control:
##  filter tree heap memopt load sort verbose
##     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
## 
## Absolute minimum support count: 220 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[10 item(s), 2201 transaction(s)] done [0.00s].
## sorting and recoding items ... [9 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [27 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].

##      lhs                rhs       support   confidence lift      count
## [1]               ----> Age=Adult 0.9504771 0.9504771  1.0000000 2092 
## [2]  Class=2nd    ----> Age=Adult 0.1185825 0.9157895  0.9635051  261 
## [3]  Class=1st    ----> Age=Adult 0.1449341 0.9815385  1.0326798  319 
## [4]  Sex=Female   ----> Age=Adult 0.1930940 0.9042553  0.9513700  425 
## [5]  Class=3rd    ----> Age=Adult 0.2848705 0.8881020  0.9343750  627 
## [6]  Survived=Yes ----> Age=Adult 0.2971377 0.9198312  0.9677574  654 
## [7]  Class=Crew   ----> Sex=Male  0.3916402 0.9740113  1.2384742  862 
## [8]  Class=Crew   ----> Age=Adult 0.4020900 1.0000000  1.0521033  885 
## [9]  Survived=No  ----> Sex=Male  0.6197183 0.9154362  1.1639949 1364 
## [10] Survived=No  ----> Age=Adult 0.6533394 0.9651007  1.0153856 1438

Visulizing all the rules

## Loading required package: grid

## Loading required package: ggplot2

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## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
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## The following object is masked from 'package:stats':
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## The following object is masked from 'package:graphics':
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