Retail sales
Mzingaye Khumalo
2024/2025
INTRODUCTION
This is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. obtained from https://archive.ics.uci.edu/dataset/352/online+retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers. The Tables, graphs and results you will see below are related to the sales that occured in the UK alone. There are several different countries listed in this dataset but my work focuses on the UK. I have also decide to focus my work on the Aprior Algorithm.
Data Preparation
library(dplyr)
library(factoextra)
library(mclust)
library(cluster)
library(ggplot2)
library(readxl)
salesdata <- read_excel("C:/Users/mzing/Desktop/USL/RETAIL SALES/online+retail/Online Retail.xlsx")
str(salesdata)## tibble [541,909 × 8] (S3: tbl_df/tbl/data.frame)
## $ InvoiceNo : chr [1:541909] "536365" "536365" "536365" "536365" ...
## $ StockCode : chr [1:541909] "85123A" "71053" "84406B" "84029G" ...
## $ Description: chr [1:541909] "WHITE HANGING HEART T-LIGHT HOLDER" "WHITE METAL LANTERN" "CREAM CUPID HEARTS COAT HANGER" "KNITTED UNION FLAG HOT WATER BOTTLE" ...
## $ Quantity : num [1:541909] 6 6 8 6 6 2 6 6 6 32 ...
## $ InvoiceDate: POSIXct[1:541909], format: "2010-12-01 08:26:00" "2010-12-01 08:26:00" ...
## $ UnitPrice : num [1:541909] 2.55 3.39 2.75 3.39 3.39 7.65 4.25 1.85 1.85 1.69 ...
## $ CustomerID : num [1:541909] 17850 17850 17850 17850 17850 ...
## $ Country : chr [1:541909] "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...#Filtering transactions to focus on United Kingdom sales.
uksalesdata <- salesdata %>%
filter(Country == "United Kingdom") %>%
filter(!is.na(InvoiceNo) & !is.na(StockCode))
head(uksalesdata)## # A tibble: 6 × 8
## InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 536365 85123A WHITE HANGING HEAR… 6 2010-12-01 08:26:00 2.55
## 2 536365 71053 WHITE METAL LANTERN 6 2010-12-01 08:26:00 3.39
## 3 536365 84406B CREAM CUPID HEARTS… 8 2010-12-01 08:26:00 2.75
## 4 536365 84029G KNITTED UNION FLAG… 6 2010-12-01 08:26:00 3.39
## 5 536365 84029E RED WOOLLY HOTTIE … 6 2010-12-01 08:26:00 3.39
## 6 536365 22752 SET 7 BABUSHKA NES… 2 2010-12-01 08:26:00 7.65
## # ℹ 2 more variables: CustomerID <dbl>, Country <chr># I would like to find out how many distinct Items are sold in the United Kingdom
n_distinct(uksalesdata$Description)## [1] 4191# We now know there are 4191 distinct items sold in the UK retail store.Analysis of One Basket
You can also embed plots, for example:
## # A tibble: 12 × 8
## InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice
## <chr> <chr> <chr> <dbl> <dttm> <dbl>
## 1 536367 84879 ASSORTED COLOUR B… 32 2010-12-01 08:34:00 1.69
## 2 536367 22745 POPPY'S PLAYHOUSE… 6 2010-12-01 08:34:00 2.1
## 3 536367 22748 POPPY'S PLAYHOUSE… 6 2010-12-01 08:34:00 2.1
## 4 536367 22749 FELTCRAFT PRINCES… 8 2010-12-01 08:34:00 3.75
## 5 536367 22310 IVORY KNITTED MUG… 6 2010-12-01 08:34:00 1.65
## 6 536367 84969 BOX OF 6 ASSORTED… 6 2010-12-01 08:34:00 4.25
## 7 536367 22623 BOX OF VINTAGE JI… 3 2010-12-01 08:34:00 4.95
## 8 536367 22622 BOX OF VINTAGE AL… 2 2010-12-01 08:34:00 9.95
## 9 536367 21754 HOME BUILDING BLO… 3 2010-12-01 08:34:00 5.95
## 10 536367 21755 LOVE BUILDING BLO… 3 2010-12-01 08:34:00 5.95
## 11 536367 21777 RECIPE BOX WITH M… 4 2010-12-01 08:34:00 7.95
## 12 536367 48187 DOORMAT NEW ENGLA… 4 2010-12-01 08:34:00 7.95
## # ℹ 2 more variables: CustomerID <dbl>, Country <chr>## [1] 12Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.
Visualizing Items in One basket
#What is the total number of items in basket_one
basket_one %>% summarise(sum(Quantity))## # A tibble: 1 × 1
## `sum(Quantity)`
## <dbl>
## 1 83#Basket_one contains 83 Items.
#Visualising the total number of contents in basket_one
ggplot(basket_one,
aes(x = reorder(Description, Quantity),
y = Quantity)) +
geom_col(fill = "steelblue") +
coord_flip() +
xlab("Items") +
ggtitle("Summary of Items in Basket one")
Total baskets and averages
#How many possible baskets?
n_items = 4191
basket_size = 10
choose(n_items, basket_size)## [1] 4.557712e+29#How many baskets are there?
n_distinct(uksalesdata$InvoiceNo)## [1] 23494#How many items are there in each basket?
basket_size <- uksalesdata %>%
group_by(InvoiceNo) %>%
summarise(n_total = n(),
n_items = n_distinct(Description))
head(basket_size)## # A tibble: 6 × 3
## InvoiceNo n_total n_items
## <chr> <int> <int>
## 1 536365 7 7
## 2 536366 2 2
## 3 536367 12 12
## 4 536368 4 4
## 5 536369 1 1
## 6 536371 1 1#What is the average basket size
basket_size %>%
summarise(
avg_total_items = mean(n_total),
avg_dist_items = mean(n_items)
)## # A tibble: 1 × 2
## avg_total_items avg_dist_items
## <dbl> <dbl>
## 1 21.1 20.6Item Frequency
#Which items appear the most in baskets
most_frequent_items <- uksalesdata %>%
group_by(Description) %>%
summarise(TotalQuantity = sum(Quantity, na.rm = TRUE)) %>%
arrange(desc(TotalQuantity))
head(most_frequent_items, 10)## # A tibble: 10 × 2
## Description TotalQuantity
## <chr> <dbl>
## 1 WORLD WAR 2 GLIDERS ASSTD DESIGNS 48326
## 2 JUMBO BAG RED RETROSPOT 43167
## 3 POPCORN HOLDER 34365
## 4 ASSORTED COLOUR BIRD ORNAMENT 33679
## 5 WHITE HANGING HEART T-LIGHT HOLDER 33193
## 6 PACK OF 12 LONDON TISSUES 25307
## 7 PACK OF 72 RETROSPOT CAKE CASES 24702
## 8 VICTORIAN GLASS HANGING T-LIGHT 23242
## 9 BROCADE RING PURSE 22801
## 10 ASSORTED COLOURS SILK FAN 20322#Based on the table above, We need to know the number of baskets that contain both items
uksalesdata %>%
filter(Description %in% c("WORLD WAR 2 GLIDERS ASSTD DESIGNS","JUMBO BAG RED RETROSPOT")) %>%
group_by(InvoiceNo) %>%
summarise(n=n()) %>%
filter(n==2) %>%
summarise(n_distinct(InvoiceNo))## # A tibble: 1 × 1
## `n_distinct(InvoiceNo)`
## <int>
## 1 106The APRIORI Function
library(arules)
library(dplyr)
#Splitting transactions
uksales_list <- split(uksalesdata$Description, uksalesdata$InvoiceNo)
#Coerce the list into a `transactions` object
uksales_trx <- as(uksales_list, "transactions")
summary(uksales_trx)## transactions as itemMatrix in sparse format with
## 23494 rows (elements/itemsets/transactions) and
## 4190 columns (items) and a density of 0.004909373
##
## most frequent items:
## WHITE HANGING HEART T-LIGHT HOLDER JUMBO BAG RED RETROSPOT
## 2206 1978
## REGENCY CAKESTAND 3 TIER PARTY BUNTING
## 1832 1613
## LUNCH BAG RED RETROSPOT (Other)
## 1430 474219
##
## element (itemset/transaction) length distribution:
## sizes
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 1454 4060 1449 994 730 711 611 606 580 578 495 503 453 444 487 495
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
## 500 420 409 433 388 362 308 314 272 224 241 214 226 244 195 166
## 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
## 169 145 158 122 119 118 106 123 110 108 92 86 89 83 86 72
## 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63
## 79 81 77 49 56 68 63 68 43 55 42 33 52 33 22 37
## 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
## 37 35 39 28 35 24 30 36 20 22 32 25 18 17 26 13
## 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
## 10 15 18 14 22 15 17 9 17 9 10 9 12 13 6 4
## 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111
## 9 8 11 4 9 8 3 6 9 2 3 7 3 4 4 7
## 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127
## 3 5 6 6 7 5 4 7 4 6 11 3 5 1 4 8
## 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
## 1 2 4 2 3 2 5 4 2 5 6 2 5 6 2 1
## 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159
## 5 5 3 2 4 4 3 5 3 6 2 2 2 4 4 1
## 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175
## 2 3 3 3 2 4 4 1 4 4 1 2 4 3 4 2
## 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191
## 5 5 4 2 4 2 6 4 3 3 3 2 3 4 4 2
## 192 193 194 195 196 197 198 199 202 203 204 205 206 207 208 210
## 3 2 3 3 4 2 2 3 2 5 5 1 2 1 4 1
## 211 212 213 214 215 216 217 218 219 220 222 223 224 225 226 227
## 4 1 1 2 1 2 4 2 1 2 1 1 3 3 1 1
## 228 229 230 232 233 234 235 237 238 239 241 242 243 244 247 249
## 1 2 1 1 1 1 1 3 3 1 2 1 2 2 2 3
## 250 253 254 255 257 261 262 263 264 266 267 270 275 279 280 282
## 2 1 2 2 2 2 2 1 2 1 1 2 1 2 2 1
## 283 285 286 288 289 291 292 295 296 298 299 301 309 310 315 319
## 2 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1
## 320 331 332 333 334 339 341 344 345 347 348 349 352 354 357 358
## 1 1 4 1 1 1 1 1 1 2 1 1 2 1 1 1
## 363 369 375 376 379 382 386 388 399 404 408 411 414 415 416 419
## 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 1
## 420 428 433 434 438 439 443 449 453 455 458 460 463 471 482 486
## 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1
## 487 488 494 499 503 506 514 515 517 518 520 522 524 525 527 529
## 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1
## 531 536 539 541 543 552 561 567 572 578 585 588 589 593 595 599
## 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
## 601 607 622 629 635 645 647 649 661 673 676 687 703 720 731 748
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1108
## 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 2.00 9.00 20.57 23.00 1108.00
##
## includes extended item information - examples:
## labels
## 1 *Boombox Ipod Classic
## 2 *USB Office Mirror Ball
## 3 ?
##
## includes extended transaction information - examples:
## transactionID
## 1 536365
## 2 536366
## 3 536367Inspections
rules.uksales <- apriori(uksales_trx,
parameter = list(supp = 0.01, conf = 0.8, minlen = 2))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 234
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[4190 item(s), 23494 transaction(s)] done [0.21s].
## sorting and recoding items ... [601 item(s)] done [0.01s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 done [0.03s].
## writing ... [29 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].#Inspect the first 5 transactions
inspect(head(rules.uksales, 5))## lhs rhs support confidence coverage lift count
## [1] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN} 0.01034307 0.8933824 0.01157742 61.914823 243
## [2] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE ROSES} 0.01008768 0.8713235 0.01157742 52.355179 237
## [3] {WOODEN TREE CHRISTMAS SCANDINAVIAN} => {WOODEN STAR CHRISTMAS SCANDINAVIAN} 0.01123691 0.8488746 0.01323742 41.376473 264
## [4] {REGENCY TEA PLATE GREEN} => {REGENCY TEA PLATE ROSES} 0.01200306 0.8318584 0.01442922 49.983840 282
## [5] {JUMBO BAG PINK POLKADOT,
## JUMBO BAG SCANDINAVIAN BLUE PAISLEY} => {JUMBO BAG RED RETROSPOT} 0.01089640 0.8366013 0.01302460 9.936861 256#inspect the first 5 rules with highest lift
inspect(head(sort(rules.uksales, by = "lift"), 5))## lhs rhs support confidence coverage lift count
## [1] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN} 0.01034307 0.8933824 0.01157742 61.91482 243
## [2] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE ROSES} 0.01008768 0.8713235 0.01157742 52.35518 237
## [3] {REGENCY TEA PLATE GREEN} => {REGENCY TEA PLATE ROSES} 0.01200306 0.8318584 0.01442922 49.98384 282
## [4] {WOODEN TREE CHRISTMAS SCANDINAVIAN} => {WOODEN STAR CHRISTMAS SCANDINAVIAN} 0.01123691 0.8488746 0.01323742 41.37647 264
## [5] {JUMBO BAG RED RETROSPOT,
## SUKI SHOULDER BAG} => {DOTCOM POSTAGE} 0.01059845 0.8924731 0.01187537 29.57371 249#Transform the rules back to a dataframe
rules.df <- as(rules.uksales, "data.frame")
#check the first record again
inspect(head(sort(rules.uksales, by = "count"), 5))## lhs rhs support confidence coverage lift count
## [1] {PINK REGENCY TEACUP AND SAUCER,
## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND SAUCER} 0.02115434 0.8922801 0.02370818 21.611575 497
## [2] {GREEN REGENCY TEACUP AND SAUCER,
## PINK REGENCY TEACUP AND SAUCER} => {ROSES REGENCY TEACUP AND SAUCER} 0.02115434 0.8495726 0.02489997 19.900159 497
## [3] {JUMBO BAG PINK POLKADOT,
## JUMBO STORAGE BAG SUKI} => {JUMBO BAG RED RETROSPOT} 0.01736614 0.8031496 0.02162254 9.539533 408
## [4] {JUMBO BAG PINK POLKADOT,
## JUMBO SHOPPER VINTAGE RED PAISLEY} => {JUMBO BAG RED RETROSPOT} 0.01566357 0.8034934 0.01949434 9.543617 368
## [5] {CHARLOTTE BAG SUKI DESIGN,
## STRAWBERRY CHARLOTTE BAG} => {RED RETROSPOT CHARLOTTE BAG} 0.01379076 0.8202532 0.01681280 20.969562 324Rule1 indicates from the LHS, If {Regency Tea plate pink} is bought there is a 1.03%(support) chance that {Regency tea plate green} will also be purchased in the same transaction. It also indicates there is an 89.35 confidence that both items are purchased. The lift of 61.91 indicates that these two items have a very strong association meaning they are most likely to be bought together. This rule appears in 243 transactions.
GRAPHS
library(arules)
library(arulesViz)
frequent_itemsets <- apriori(
data = uksales_trx,
parameter = list(supp = 0.01, target = "frequent itemsets")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## NA 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 10 frequent itemsets TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 234
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[4190 item(s), 23494 transaction(s)] done [0.20s].
## sorting and recoding items ... [601 item(s)] done [0.01s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 done [0.02s].
## sorting transactions ... done [0.00s].
## writing ... [1115 set(s)] done [0.00s].
## creating S4 object ... done [0.00s].#The data shows that this is the most purchased item "WHITE HANGING HEART T-LIGHT HOLDER" with a 9.39% support.
#Inspect the top 10 frequent itemsets
inspect(head(sort(frequent_itemsets, by = "support"), 10))## items support count
## [1] {WHITE HANGING HEART T-LIGHT HOLDER} 0.09389631 2206
## [2] {JUMBO BAG RED RETROSPOT} 0.08419171 1978
## [3] {REGENCY CAKESTAND 3 TIER} 0.07797736 1832
## [4] {PARTY BUNTING} 0.06865583 1613
## [5] {LUNCH BAG RED RETROSPOT} 0.06086660 1430
## [6] {ASSORTED COLOUR BIRD ORNAMENT} 0.05882353 1382
## [7] {SET OF 3 CAKE TINS PANTRY DESIGN} 0.05537584 1301
## [8] {LUNCH BAG BLACK SKULL.} 0.05265174 1237
## [9] {NATURAL SLATE HEART CHALKBOARD} 0.05260918 1236
## [10] {HEART OF WICKER SMALL} 0.05001277 1175rules <- apriori(
data = uksales_trx,
parameter = list(supp = 0.01, conf = 0.8, minlen = 2)
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 234
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[4190 item(s), 23494 transaction(s)] done [0.19s].
## sorting and recoding items ... [601 item(s)] done [0.01s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 done [0.03s].
## writing ... [29 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].# Sort rules by lift and inspect the top 10
sorted_rules <- sort(rules, by = "lift")
inspect(head(sorted_rules, 10))## lhs rhs support confidence coverage lift count
## [1] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN} 0.01034307 0.8933824 0.01157742 61.91482 243
## [2] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE ROSES} 0.01008768 0.8713235 0.01157742 52.35518 237
## [3] {REGENCY TEA PLATE GREEN} => {REGENCY TEA PLATE ROSES} 0.01200306 0.8318584 0.01442922 49.98384 282
## [4] {WOODEN TREE CHRISTMAS SCANDINAVIAN} => {WOODEN STAR CHRISTMAS SCANDINAVIAN} 0.01123691 0.8488746 0.01323742 41.37647 264
## [5] {JUMBO BAG RED RETROSPOT,
## SUKI SHOULDER BAG} => {DOTCOM POSTAGE} 0.01059845 0.8924731 0.01187537 29.57371 249
## [6] {RED RETROSPOT CHARLOTTE BAG,
## STRAWBERRY CHARLOTTE BAG,
## WOODLAND CHARLOTTE BAG} => {CHARLOTTE BAG SUKI DESIGN} 0.01064101 0.8169935 0.01302460 23.58040 250
## [7] {CHARLOTTE BAG PINK POLKADOT,
## CHARLOTTE BAG SUKI DESIGN,
## STRAWBERRY CHARLOTTE BAG} => {RED RETROSPOT CHARLOTTE BAG} 0.01021537 0.9160305 0.01115178 23.41809 240
## [8] {CHARLOTTE BAG PINK POLKADOT,
## RED RETROSPOT CHARLOTTE BAG,
## WOODLAND CHARLOTTE BAG} => {CHARLOTTE BAG SUKI DESIGN} 0.01025794 0.8006645 0.01281178 23.10910 241
## [9] {CHARLOTTE BAG PINK POLKADOT,
## CHARLOTTE BAG SUKI DESIGN,
## WOODLAND CHARLOTTE BAG} => {RED RETROSPOT CHARLOTTE BAG} 0.01025794 0.8700361 0.01179024 22.24225 241
## [10] {CHARLOTTE BAG PINK POLKADOT,
## STRAWBERRY CHARLOTTE BAG} => {RED RETROSPOT CHARLOTTE BAG} 0.01349281 0.8684932 0.01553588 22.20281 317itemFrequencyPlot(uksales_trx, topN = 10, type = "absolute",
main = "Top 10 Frequent Items",
col = rainbow (10),
ylab = "",
horiz = T)
this table shows the most frequently bought items.
Interactive Table
inspectDT(rules)top5subrules <- head(sort(rules, by = "confidence"), 5)
inspect(top5subrules)## lhs rhs support confidence coverage lift count
## [1] {CHARLOTTE BAG PINK POLKADOT,
## CHARLOTTE BAG SUKI DESIGN,
## STRAWBERRY CHARLOTTE BAG} => {RED RETROSPOT CHARLOTTE BAG} 0.01021537 0.9160305 0.01115178 23.41809 240
## [2] {PINK REGENCY TEACUP AND SAUCER,
## REGENCY CAKESTAND 3 TIER,
## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND SAUCER} 0.01123691 0.8979592 0.01251383 21.74913 264
## [3] {REGENCY TEA PLATE PINK} => {REGENCY TEA PLATE GREEN} 0.01034307 0.8933824 0.01157742 61.91482 243
## [4] {JUMBO BAG RED RETROSPOT,
## SUKI SHOULDER BAG} => {DOTCOM POSTAGE} 0.01059845 0.8924731 0.01187537 29.57371 249
## [5] {PINK REGENCY TEACUP AND SAUCER,
## ROSES REGENCY TEACUP AND SAUCER} => {GREEN REGENCY TEACUP AND SAUCER} 0.02115434 0.8922801 0.02370818 21.61158 497Based on the inspection of top5subrules,
Rule1 indicates: when a customer buys {CHARLOTTE BAG PINK POLKADOT, CHARLOTTE BAG SUKI DESIGN, STRAWBERRY CHARLOTTE BAG}, they are very likely (91.6% confidence) to also buy {RED RETROSPOT CHARLOTTE BAG}. The lift for this relationship is very high 23.42, which shows a significant dependancy.
Rule3 indicates: When a customer buys {REGENCY TEA PLATE PINK} there’s an 89.3% chance that {REGENCY TEA PLATE GREEN} is also purchased. The lift (61.91) indicates an exceptionally strong relationship.
Rules2,4 and 5 can be interpreted in a like manner. Feel free to use the interactive table to see the relationships between other products.