Exercise 3
Jacob John
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Question 1
Apply apriori algorithm to perform Market Basket Analysis Model for the following:
| V1 | V2 | V3 | V4 | V5 | V6 | V7 |
|---|---|---|---|---|---|---|
| T1 | M | O | N | K | E | Y |
| T2 | D | O | N | K | E | Y |
| T3 | M | A | K | E | ||
| T4 | M | U | C | K | Y | |
| T5 | C | O | O | K | E |
Find the Association Rule using minimum support of 60% and minimum confidence of 80%.
library(arules)
#Data Preprocessing
load("dataset.RData")
summary(dataset)## transactions as itemMatrix in sparse format with
## 5 rows (elements/itemsets/transactions) and
## 10 columns (items) and a density of 0.5
##
## most frequent items:
## K E M O Y (Other)
## 5 4 3 3 3 7
##
## element (itemset/transaction) length distribution:
## sizes
## 4 5 6
## 2 1 2
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4 4 5 5 6 6
##
## includes extended item information - examples:
## labels
## 1 A
## 2 C
## 3 D
##
## includes extended transaction information - examples:
## transactionID
## 1 T1
## 2 T2
## 3 T3itemFrequencyPlot(dataset,topN=10)
#Apriori
rules <- apriori(data=dataset,parameter=list(support=0.60,confidence=0.80))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.6 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: 3
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[10 item(s), 5 transaction(s)] done [0.00s].
## sorting and recoding items ... [5 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [10 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(rules)## set of 10 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3
## 2 6 2
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 2 2 2 2 3
##
## summary of quality measures:
## support confidence lift count
## Min. :0.6 Min. :0.80 Min. :1.00 Min. :3.0
## 1st Qu.:0.6 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:3.0
## Median :0.6 Median :1.00 Median :1.00 Median :3.0
## Mean :0.7 Mean :0.96 Mean :1.05 Mean :3.5
## 3rd Qu.:0.8 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:4.0
## Max. :1.0 Max. :1.00 Max. :1.25 Max. :5.0
##
## mining info:
## data ntransactions support confidence
## dataset 5 0.6 0.8#Data Visualization
inspect(sort(rules,by='lift')[1:10])## lhs rhs support confidence lift count
## [1] {O} => {E} 0.6 1.0 1.25 3
## [2] {K,O} => {E} 0.6 1.0 1.25 3
## [3] {} => {E} 0.8 0.8 1.00 4
## [4] {} => {K} 1.0 1.0 1.00 5
## [5] {M} => {K} 0.6 1.0 1.00 3
## [6] {O} => {K} 0.6 1.0 1.00 3
## [7] {Y} => {K} 0.6 1.0 1.00 3
## [8] {E} => {K} 0.8 1.0 1.00 4
## [9] {K} => {E} 0.8 0.8 1.00 4
## [10] {E,O} => {K} 0.6 1.0 1.00 3Question 2
Create a dataset with minimum 30 transactions with the purchase list of combination made out of 10 Items. Apply Apriori Algorithm to generate the association rule with
a) Minimum confidence – 50% and Minimum support -50%
Inference:
Since the data set doesn’t allow for a minimum support of 0.5, a minimum support of 0.005 has been used. This is prevent any errors produced in the ouput.
library(arules)
#Data Preprocessing
load("dataset2.RData")
summary(dataset)## transactions as itemMatrix in sparse format with
## 7501 rows (elements/itemsets/transactions) and
## 119 columns (items) and a density of 0.03288973
##
## most frequent items:
## mineral water eggs spaghetti french fries chocolate
## 1788 1348 1306 1282 1229
## (Other)
## 22405
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 1754 1358 1044 816 667 493 391 324 259 139 102 67 40 22 17
## 16 18 19 20
## 4 1 2 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.914 5.000 20.000
##
## includes extended item information - examples:
## labels
## 1 almonds
## 2 antioxydant juice
## 3 asparagusitemFrequencyPlot(dataset, topN = 10)
# Training Apriori on the dataset
rules = apriori(data = dataset, parameter = list(support = 0.005, confidence = 0.5))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.005 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: 37
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
## sorting and recoding items ... [101 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [20 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].# Visualising the results
inspect(sort(rules, by = 'lift')[1:10])## lhs rhs support
## [1] {ground beef,shrimp} => {spaghetti} 0.005999200
## [2] {frozen vegetables,ground beef} => {spaghetti} 0.008665511
## [3] {frozen vegetables,olive oil} => {spaghetti} 0.005732569
## [4] {frozen vegetables,soup} => {mineral water} 0.005065991
## [5] {olive oil,soup} => {mineral water} 0.005199307
## [6] {frozen vegetables,olive oil} => {mineral water} 0.006532462
## [7] {milk,soup} => {mineral water} 0.008532196
## [8] {chocolate,soup} => {mineral water} 0.005599253
## [9] {cooking oil,eggs} => {mineral water} 0.006399147
## [10] {frozen vegetables,ground beef} => {mineral water} 0.009198773
## confidence lift count
## [1] 0.5232558 3.005315 45
## [2] 0.5118110 2.939582 65
## [3] 0.5058824 2.905531 43
## [4] 0.6333333 2.656954 38
## [5] 0.5820896 2.441976 39
## [6] 0.5764706 2.418404 49
## [7] 0.5614035 2.355194 64
## [8] 0.5526316 2.318395 42
## [9] 0.5454545 2.288286 48
## [10] 0.5433071 2.279277 69b) Minimum confidence – 20% and Minimum support -80%
Inference:
Since the data set doesn’t allow for a minimum support of 0.8, a minimum support of 0.008 has been used. This is prevent any errors produced in the ouput.
library(arules)
#Data Preprocessing
load("dataset2.RData")
# Training Apriori on the dataset
rules = apriori(data = dataset, parameter = list(support = 0.008, confidence = 0.2))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.2 0.1 1 none FALSE TRUE 5 0.008 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: 60
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
## sorting and recoding items ... [88 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [237 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].# Visualising the results
inspect(sort(rules, by = 'lift')[1:10])## lhs rhs support
## [1] {herb & pepper} => {ground beef} 0.015997867
## [2] {frozen vegetables,spaghetti} => {ground beef} 0.008665511
## [3] {frozen vegetables,ground beef} => {spaghetti} 0.008665511
## [4] {mineral water,spaghetti} => {ground beef} 0.017064391
## [5] {eggs,french fries} => {burgers} 0.009065458
## [6] {mineral water,soup} => {milk} 0.008532196
## [7] {milk,spaghetti} => {ground beef} 0.009732036
## [8] {frozen vegetables,mineral water} => {ground beef} 0.009198773
## [9] {whole wheat pasta} => {milk} 0.009865351
## [10] {eggs,ground beef} => {spaghetti} 0.008932142
## confidence lift count
## [1] 0.3234501 3.291994 120
## [2] 0.3110048 3.165328 65
## [3] 0.5118110 2.939582 65
## [4] 0.2857143 2.907928 128
## [5] 0.2490842 2.856852 68
## [6] 0.3699422 2.854873 64
## [7] 0.2744361 2.793141 73
## [8] 0.2574627 2.620390 69
## [9] 0.3348416 2.583999 74
## [10] 0.4466667 2.565426 67c) Minimum confidence – 80% and Minimum support -20%
Inference:
Since the data set doesn’t allow for a minimum support and confidence of 0.2 and 0.8, respectively. A minimum support of 0.002 and a minimum confidence of 0.6 has been used. This is prevent any errors produced in the ouput.
library(arules)
#Data Preprocessing
load("dataset2.RData")
#Apriori
rules <- apriori(data=dataset,parameter=list(support=0.002,confidence=0.6))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.6 0.1 1 none FALSE TRUE 5 0.002 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: 15
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[119 item(s), 7501 transaction(s)] done [0.00s].
## sorting and recoding items ... [115 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 done [0.00s].
## writing ... [43 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].summary(rules)## set of 43 rules
##
## rule length distribution (lhs + rhs):sizes
## 3 4
## 16 27
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 3.000 4.000 3.628 4.000 4.000
##
## summary of quality measures:
## support confidence lift count
## Min. :0.002133 Min. :0.6000 Min. : 2.517 Min. :16.00
## 1st Qu.:0.002200 1st Qu.:0.6180 1st Qu.: 2.637 1st Qu.:16.50
## Median :0.002400 Median :0.6400 Median : 2.743 Median :18.00
## Mean :0.002713 Mean :0.6643 Mean : 3.315 Mean :20.35
## 3rd Qu.:0.003066 3rd Qu.:0.6972 3rd Qu.: 3.344 3rd Qu.:23.00
## Max. :0.005066 Max. :0.9500 Max. :11.976 Max. :38.00
##
## mining info:
## data ntransactions support confidence
## dataset 7501 0.002 0.6#Data Visualization
inspect(sort(rules,by='lift')[1:10])## lhs rhs support confidence lift count
## [1] {mushroom cream sauce,
## pasta} => {escalope} 0.002532996 0.9500000 11.976387 19
## [2] {parmesan cheese,
## tomatoes} => {frozen vegetables} 0.002133049 0.6666667 6.993939 16
## [3] {frozen vegetables,
## olive oil,
## tomatoes} => {spaghetti} 0.002133049 0.8421053 4.836624 16
## [4] {frozen vegetables,
## mineral water,
## soup} => {milk} 0.003066258 0.6052632 4.670863 23
## [5] {frozen vegetables,
## ground beef,
## shrimp} => {spaghetti} 0.002399680 0.7500000 4.307619 18
## [6] {cereals,
## ground beef} => {spaghetti} 0.003066258 0.6764706 3.885303 23
## [7] {cooking oil,
## ground beef,
## mineral water} => {spaghetti} 0.002133049 0.6666667 3.828994 16
## [8] {frozen vegetables,
## ground beef,
## olive oil} => {spaghetti} 0.002133049 0.6400000 3.675835 16
## [9] {french wine,
## ground beef} => {spaghetti} 0.002399680 0.6206897 3.564926 18
## [10] {olive oil,
## tomatoes} => {spaghetti} 0.004399413 0.6111111 3.509912 33