Association Rules Project
Anna Lorenz
2024/2025
Introduction
Understanding how marketing works begins with analysing customer behavior. It is important to identify the key factors that influence purchasing decisions and how, as sellers we can use these insights to optimise sales strategies. Identifying such patterns in the market can uncover valuable opportunities for sales improvement and business growth.
To achieve this, I apply association rule mining to analyse relationships between different factors. This approach helps uncover patterns in product categorization and potential market trends, allowing businesses to make data-driven decisions.
Dataset
For this analysis I used the Market Data. This dataset contains transactional data representing customer purchases, making it suitable for market basket analysis.
Each row in the dataset represents a customer’s shopping basket, containing a list of products purchased in a single transaction. By analysing these transactions, we can identify frequently bought-together products, helping businesses optimise product placement, cross-selling strategies, and targeted promotions.
Analysis
Loading useful packages.
library(arules)
library(ggplot2)
library(arulesViz)Loading Market Data dataset.
market <- read.csv("market_new.csv")
head(market)## Bread Honey Bacon Toothpaste Banana Apple Hazelnut Cheese Meat Carrot
## 1 1 0 1 0 1 1 1 0 0 1
## 2 1 1 1 0 1 1 1 0 0 0
## 3 0 1 1 1 1 1 1 1 1 0
## 4 1 1 0 1 0 1 0 0 0 0
## 5 0 1 0 0 0 0 0 0 0 0
## 6 0 1 0 1 0 0 1 0 0 0
## Cucumber Onion Milk Butter ShavingFoam Salt Flour HeavyCream Egg Olive
## 1 0 0 0 0 0 0 0 1 1 0
## 2 1 0 1 1 0 0 1 0 0 1
## 3 1 1 1 0 1 1 1 1 1 0
## 4 1 1 1 0 0 0 1 0 1 1
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 1 0 0 1 0 0 0 0 0
## Shampoo Sugar
## 1 0 1
## 2 1 0
## 3 0 1
## 4 1 0
## 5 0 0
## 6 0 1Checking if there are any missing values.
colSums(is.na(market))## Bread Honey Bacon Toothpaste Banana Apple
## 0 0 0 0 0 0
## Hazelnut Cheese Meat Carrot Cucumber Onion
## 0 0 0 0 0 0
## Milk Butter ShavingFoam Salt Flour HeavyCream
## 0 0 0 0 0 0
## Egg Olive Shampoo Sugar
## 0 0 0 0There are no missing values in the dataset, so we can proceed without any changes in dataset.
## Warning: pakiet 'arules' został zbudowany w wersji R 4.4.2## Ładowanie wymaganego pakietu: Matrix##
## Dołączanie pakietu: 'arules'## Następujące obiekty zostały zakryte z 'package:base':
##
## abbreviate, writeConverting dataset into transaction format.
market <- as(as.matrix(market), "transactions")
summary(market)## transactions as itemMatrix in sparse format with
## 10464 rows (elements/itemsets/transactions) and
## 22 columns (items) and a density of 0.4945528
##
## most frequent items:
## Bread Olive Butter Flour Salt (Other)
## 5296 5235 5208 5206 5204 87701
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 19 22 14 46 112 202 436 821 1220 1579 1743 1567 1237 750 414 191
## 17 18 19 20 21
## 71 15 3 1 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 9.00 11.00 10.88 13.00 21.00
##
## includes extended item information - examples:
## labels
## 1 Bread
## 2 Honey
## 3 Bacon- The most frequently purchased product is bread, which is expected
since it is a staple food.
- The minimum number of products per basket is 1, while the maximum is
21.
- The most popular basket size contains 11 products.
size(market[1:100])## [1] 9 12 17 11 1 6 6 8 14 6 10 9 15 13 10 4 3 4 13 14 4 9 10 8 7
## [26] 12 5 9 12 9 13 14 12 14 9 12 7 2 9 8 4 4 5 12 10 9 1 12 12 11
## [51] 12 11 6 9 14 10 7 15 3 13 9 10 6 12 15 13 12 10 12 14 13 8 11 11 6
## [76] 4 1 14 13 13 8 7 2 5 6 9 5 11 14 3 10 4 14 10 9 4 6 13 15 8Dataset contains 10,464 transactions (baskets) and 22 products. This provides a good representation for analysis.
inspect(market[1:50])## items
## [1] {Bread,
## Bacon,
## Banana,
## Apple,
## Hazelnut,
## Carrot,
## HeavyCream,
## Egg,
## Sugar}
## [2] {Bread,
## Honey,
## Bacon,
## Banana,
## Apple,
## Hazelnut,
## Cucumber,
## Milk,
## Butter,
## Flour,
## Olive,
## Shampoo}
## [3] {Honey,
## Bacon,
## Toothpaste,
## Banana,
## Apple,
## Hazelnut,
## Cheese,
## Meat,
## Cucumber,
## Onion,
## Milk,
## ShavingFoam,
## Salt,
## Flour,
## HeavyCream,
## Egg,
## Sugar}
## [4] {Bread,
## Honey,
## Toothpaste,
## Apple,
## Cucumber,
## Onion,
## Milk,
## Flour,
## Egg,
## Olive,
## Shampoo}
## [5] {Honey}
## [6] {Honey,
## Toothpaste,
## Hazelnut,
## Onion,
## ShavingFoam,
## Sugar}
## [7] {Bacon,
## Banana,
## Apple,
## Carrot,
## Salt,
## HeavyCream}
## [8] {Bacon,
## Toothpaste,
## Banana,
## Hazelnut,
## Cucumber,
## Onion,
## Salt,
## Egg}
## [9] {Honey,
## Bacon,
## Banana,
## Apple,
## Hazelnut,
## Cheese,
## Meat,
## Carrot,
## Onion,
## Butter,
## ShavingFoam,
## HeavyCream,
## Egg,
## Olive}
## [10] {Onion,
## Milk,
## Salt,
## Flour,
## HeavyCream,
## Shampoo}
## [11] {Bacon,
## Toothpaste,
## Apple,
## Hazelnut,
## Carrot,
## Butter,
## Flour,
## HeavyCream,
## Egg,
## Olive}
## [12] {Honey,
## Bacon,
## Toothpaste,
## Meat,
## Onion,
## Butter,
## Flour,
## HeavyCream,
## Sugar}
## [13] {Bread,
## Honey,
## Bacon,
## Toothpaste,
## Banana,
## Apple,
## Meat,
## Carrot,
## Onion,
## Milk,
## ShavingFoam,
## Egg,
## Olive,
## Shampoo,
## Sugar}
## [14] {Bread,
## Bacon,
## Toothpaste,
## Banana,
## Hazelnut,
## Cheese,
## Carrot,
## ShavingFoam,
## Salt,
## HeavyCream,
## Egg,
## Olive,
## Sugar}
## [15] {Toothpaste,
## Banana,
## Carrot,
## Cucumber,
## ShavingFoam,
## Flour,
## Egg,
## Olive,
## Shampoo,
## Sugar}
## [16] {Bacon,
## Milk,
## Flour,
## Shampoo}
## [17] {Toothpaste,
## Cucumber,
## Flour}
## [18] {Onion,
## ShavingFoam,
## Salt,
## Olive}
## [19] {Bread,
## Honey,
## Bacon,
## Toothpaste,
## Hazelnut,
## Cheese,
## Meat,
## Carrot,
## Onion,
## Butter,
## ShavingFoam,
## Salt,
## Sugar}
## [20] {Bread,
## Bacon,
## Toothpaste,
## Banana,
## Hazelnut,
## Cheese,
## Carrot,
## Onion,
## Butter,
## ShavingFoam,
## Salt,
## Flour,
## Olive,
## Sugar}
## [21] {Cheese,
## ShavingFoam,
## Shampoo,
## Sugar}
## [22] {Banana,
## Cheese,
## Meat,
## Carrot,
## Onion,
## Salt,
## HeavyCream,
## Egg,
## Olive}
## [23] {Bread,
## Honey,
## Bacon,
## Toothpaste,
## Cheese,
## Carrot,
## Onion,
## Butter,
## ShavingFoam,
## Egg}
## [24] {Honey,
## Toothpaste,
## Meat,
## Carrot,
## Onion,
## HeavyCream,
## Egg,
## Shampoo}
## [25] {Honey,
## Toothpaste,
## Banana,
## Hazelnut,
## Cheese,
## HeavyCream,
## Olive}
## [26] {Honey,
## Bacon,
## Apple,
## Hazelnut,
## Cheese,
## Meat,
## Onion,
## Butter,
## ShavingFoam,
## Salt,
## Shampoo,
## Sugar}
## [27] {Honey,
## Hazelnut,
## Cheese,
## Milk,
## Shampoo}
## [28] {Bread,
## Bacon,
## Toothpaste,
## Apple,
## Carrot,
## Butter,
## ShavingFoam,
## Shampoo,
## Sugar}
## [29] {Bread,
## Honey,
## Bacon,
## Cheese,
## Meat,
## Cucumber,
## Onion,
## Milk,
## ShavingFoam,
## Salt,
## HeavyCream,
## Sugar}
## [30] {Bread,
## Honey,
## Apple,
## Meat,
## Carrot,
## Salt,
## HeavyCream,
## Egg,
## Shampoo}
## [31] {Bacon,
## Toothpaste,
## Apple,
## Hazelnut,
## Meat,
## Cucumber,
## Onion,
## Butter,
## ShavingFoam,
## Flour,
## Egg,
## Olive,
## Sugar}
## [32] {Honey,
## Toothpaste,
## Banana,
## Apple,
## Hazelnut,
## Cheese,
## Meat,
## Carrot,
## Cucumber,
## Onion,
## Milk,
## Flour,
## Egg,
## Olive}
## [33] {Bread,
## Honey,
## Toothpaste,
## Banana,
## Apple,
## Hazelnut,
## Milk,
## Butter,
## Salt,
## Flour,
## HeavyCream,
## Shampoo}
## [34] {Bread,
## Bacon,
## Banana,
## Apple,
## Hazelnut,
## Cheese,
## Carrot,
## Cucumber,
## Onion,
## Milk,
## Salt,
## HeavyCream,
## Egg,
## Olive}
## [35] {Bread,
## Bacon,
## Toothpaste,
## Banana,
## Hazelnut,
## Meat,
## Cucumber,
## Salt,
## Sugar}
## [36] {Bread,
## Bacon,
## Apple,
## Hazelnut,
## Meat,
## Milk,
## Salt,
## Flour,
## HeavyCream,
## Olive,
## Shampoo,
## Sugar}
## [37] {Bread,
## Toothpaste,
## Cheese,
## Milk,
## Salt,
## Flour,
## Egg}
## [38] {Bacon,
## Olive}
## [39] {Bread,
## Bacon,
## Toothpaste,
## Banana,
## Hazelnut,
## Carrot,
## Butter,
## ShavingFoam,
## Olive}
## [40] {Banana,
## Apple,
## Hazelnut,
## Cheese,
## Meat,
## Cucumber,
## Salt,
## Sugar}
## [41] {Cucumber,
## Salt,
## HeavyCream,
## Sugar}
## [42] {Apple,
## Milk,
## HeavyCream,
## Olive}
## [43] {Bread,
## Cheese,
## Onion,
## Flour,
## Olive}
## [44] {Bread,
## Bacon,
## Banana,
## Cheese,
## Carrot,
## Cucumber,
## Onion,
## Milk,
## Butter,
## Salt,
## Egg,
## Olive}
## [45] {Honey,
## Toothpaste,
## Banana,
## Apple,
## Meat,
## Carrot,
## Milk,
## Salt,
## Flour,
## Olive}
## [46] {Bread,
## Honey,
## Hazelnut,
## Cheese,
## Meat,
## Carrot,
## Butter,
## ShavingFoam,
## Olive}
## [47] {Bacon}
## [48] {Bacon,
## Toothpaste,
## Banana,
## Apple,
## Hazelnut,
## Meat,
## Carrot,
## Salt,
## Flour,
## HeavyCream,
## Olive,
## Sugar}
## [49] {Bacon,
## Toothpaste,
## Banana,
## Cheese,
## Meat,
## Carrot,
## Cucumber,
## Onion,
## Butter,
## Flour,
## Egg,
## Shampoo}
## [50] {Bread,
## Honey,
## Toothpaste,
## Hazelnut,
## Meat,
## Onion,
## Milk,
## Butter,
## Flour,
## Olive,
## Shampoo}Item frequency
itemFrequency(market, type = "absolute")## Bread Honey Bacon Toothpaste Banana Apple
## 5296 5127 5196 5072 5167 5092
## Hazelnut Cheese Meat Carrot Cucumber Onion
## 5133 5158 5172 5109 5189 5198
## Milk Butter ShavingFoam Salt Flour HeavyCream
## 5185 5208 5193 5204 5206 5194
## Egg Olive Shampoo Sugar
## 5202 5235 5154 5160itemFrequency(market, type ="relative")## Bread Honey Bacon Toothpaste Banana Apple
## 0.5061162 0.4899656 0.4965596 0.4847095 0.4937882 0.4866208
## Hazelnut Cheese Meat Carrot Cucumber Onion
## 0.4905390 0.4929281 0.4942661 0.4882454 0.4958907 0.4967508
## Milk Butter ShavingFoam Salt Flour HeavyCream
## 0.4955084 0.4977064 0.4962729 0.4973242 0.4975153 0.4963685
## Egg Olive Shampoo Sugar
## 0.4971330 0.5002867 0.4925459 0.4931193itemFrequencyPlot(market, topN = 10, ylab = "Item frequency [relative]", type = "relative", col = "darkseagreen3") 
There is similar frequency for all of this ten transactions, probably it is caused by the fact, that the most frequent basket is for 11 products (it is half of all factors from datasets).
Visualising transactions
image(sample(market, 100))
image(sample(market, 150))
It is difficult to identify strong item relationships visually because of the large baskets, so we will search for the relations by applying association rule mining using the Apriori algorithm.
Apriori algorithm
The Apriori algorithm is used to discover relationships between products in shopping baskets. It uses the following key metrics: - Support: The frequency of an itemset appearing in transactions. - Confidence: The probability of purchasing an item given another item is purchased. - Lift: The strength of an association compared to a random occurrence.
We set the parameters as follows: - Support = 0.2 (The itemset appears in at least 20% of transactions). - Confidence = 0.5 (The association holds at least 50% of the time). - Minlen = 2 (Rules must contain at least 2 items).
I have decided to choose these high support and confidence values because our dataset has large baskets compared to the number of factors. When I used lower values, thousands of rules were generated, making interpretation more difficult.
market_rules <- apriori(market, parameter = list(support = 0.2, confidence = 0.5, minlen = 2))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.2 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: 2092
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[22 item(s), 10464 transaction(s)] done [0.01s].
## sorting and recoding items ... [22 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.01s].
## writing ... [168 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].market_rules## set of 168 rulesWe have 168 rules determined by algorithm.
Top 10 strongest rules
- Lift shows the strongest product relationships.
- Support highlights the most frequent product combinations.
- Confidence shows the probability of an item being bought when another is purchased.
inspect(sort(market_rules, by = "lift")[1:10])## lhs rhs support confidence coverage lift
## [1] {Cucumber} => {Onion} 0.2546827 0.5135864 0.4958907 1.033892
## [2] {Onion} => {Cucumber} 0.2546827 0.5126972 0.4967508 1.033892
## [3] {Bread} => {Salt} 0.2594610 0.5126511 0.5061162 1.030819
## [4] {Salt} => {Bread} 0.2594610 0.5217141 0.4973242 1.030819
## [5] {Sugar} => {Meat} 0.2511468 0.5093023 0.4931193 1.030421
## [6] {Meat} => {Sugar} 0.2511468 0.5081206 0.4942661 1.030421
## [7] {Apple} => {Meat} 0.2467508 0.5070699 0.4866208 1.025905
## [8] {ShavingFoam} => {HeavyCream} 0.2524847 0.5087618 0.4962729 1.024968
## [9] {HeavyCream} => {ShavingFoam} 0.2524847 0.5086638 0.4963685 1.024968
## [10] {Apple} => {Banana} 0.2461774 0.5058916 0.4866208 1.024511
## count
## [1] 2665
## [2] 2665
## [3] 2715
## [4] 2715
## [5] 2628
## [6] 2628
## [7] 2582
## [8] 2642
## [9] 2642
## [10] 2576inspect(sort(market_rules, by = "support")[1:10])## lhs rhs support confidence coverage lift count
## [1] {Salt} => {Bread} 0.2594610 0.5217141 0.4973242 1.030819 2715
## [2] {Bread} => {Salt} 0.2594610 0.5126511 0.5061162 1.030819 2715
## [3] {HeavyCream} => {Bread} 0.2553517 0.5144397 0.4963685 1.016446 2672
## [4] {Bread} => {HeavyCream} 0.2553517 0.5045317 0.5061162 1.016446 2672
## [5] {Egg} => {Bread} 0.2550650 0.5130719 0.4971330 1.013743 2669
## [6] {Bread} => {Egg} 0.2550650 0.5039653 0.5061162 1.013743 2669
## [7] {Cucumber} => {Onion} 0.2546827 0.5135864 0.4958907 1.033892 2665
## [8] {Onion} => {Cucumber} 0.2546827 0.5126972 0.4967508 1.033892 2665
## [9] {Olive} => {Bread} 0.2545872 0.5088825 0.5002867 1.005466 2664
## [10] {Bread} => {Olive} 0.2545872 0.5030211 0.5061162 1.005466 2664Following the table, bread and salt are the most popular products in the market and are most often bought together.
inspect(sort(market_rules, by = "confidence")[1:10])## lhs rhs support confidence coverage lift count
## [1] {Salt} => {Bread} 0.2594610 0.5217141 0.4973242 1.030819 2715
## [2] {HeavyCream} => {Bread} 0.2553517 0.5144397 0.4963685 1.016446 2672
## [3] {Carrot} => {Bread} 0.2511468 0.5143864 0.4882454 1.016340 2628
## [4] {Cucumber} => {Onion} 0.2546827 0.5135864 0.4958907 1.033892 2665
## [5] {Egg} => {Bread} 0.2550650 0.5130719 0.4971330 1.013743 2669
## [6] {Onion} => {Cucumber} 0.2546827 0.5126972 0.4967508 1.033892 2665
## [7] {Bread} => {Salt} 0.2594610 0.5126511 0.5061162 1.030819 2715
## [8] {Toothpaste} => {Olive} 0.2483754 0.5124211 0.4847095 1.024255 2599
## [9] {Bacon} => {Bread} 0.2541093 0.5117398 0.4965596 1.011111 2659
## [10] {Hazelnut} => {Bread} 0.2508601 0.5113968 0.4905390 1.010434 2625Following the table, bread is most likely to be bought with salt, heavy cream or carrot (with probability 52.17%, 51.44%, 51.43 %).
Visualisation of association rules
## Warning: pakiet 'arulesViz' został zbudowany w wersji R 4.4.2plot(market_rules, colors = c("tomato1", "darkseagreen4"))
plot(market_rules, method = "grouped", control=list(col = c("tomato1", "darkseagreen4")))
plot(market_rules, method = "graph", colors = c("tomato1", "darkseagreen4"))
Following the plot, larger nodes indicate higher support, and colour intensity indicate the strength of association - lift.
Bread and salt with sugar and cucumber, shows strong relation, suggesting key shopping patterns for sellers.
plot(market_rules, method = "paracoord", control = list(reorder = TRUE))
Analysis of rules for bread
Since the bread is the most frequently purchased product it is worth to look closely into its analysis.
rules_bread <- apriori(data = market, parameter = list(supp = 0.01,conf = 0.005),
appearance =list(default ="lhs", rhs = "Bread"), control = list(verbose = F))
plot(rules_bread, method = "graph", colors = c("tomato1", "darkseagreen4"))
inspect(sort(rules_bread, by = "lift")[1:10])## lhs rhs support confidence coverage lift count
## [1] {Bacon,
## Hazelnut,
## Carrot,
## Cucumber,
## Milk,
## Salt} => {Bread} 0.01032110 0.6625767 0.01557722 1.309139 108
## [2] {Honey,
## Hazelnut,
## Onion,
## HeavyCream,
## Egg,
## Shampoo} => {Bread} 0.01118119 0.6536313 0.01710627 1.291465 117
## [3] {Honey,
## Bacon,
## Hazelnut,
## Carrot,
## Milk,
## Salt} => {Bread} 0.01012997 0.6463415 0.01567278 1.277061 106
## [4] {Bacon,
## Hazelnut,
## Carrot,
## Cucumber,
## Salt,
## Shampoo} => {Bread} 0.01012997 0.6424242 0.01576835 1.269322 106
## [5] {Banana,
## Apple,
## Milk,
## ShavingFoam,
## Salt,
## Flour} => {Bread} 0.01051223 0.6395349 0.01643731 1.263613 110
## [6] {Honey,
## Hazelnut,
## Onion,
## Salt,
## HeavyCream,
## Shampoo} => {Bread} 0.01089450 0.6333333 0.01720183 1.251360 114
## [7] {Bacon,
## Apple,
## Hazelnut,
## Carrot,
## Cucumber,
## Salt} => {Bread} 0.01032110 0.6279070 0.01643731 1.240638 108
## [8] {Hazelnut,
## Onion,
## Milk,
## Salt,
## HeavyCream,
## Egg} => {Bread} 0.01012997 0.6272189 0.01615061 1.239278 106
## [9] {Honey,
## Banana,
## Hazelnut,
## Cheese,
## Cucumber,
## HeavyCream} => {Bread} 0.01041667 0.6228571 0.01672401 1.230660 109
## [10] {Honey,
## Hazelnut,
## Cucumber,
## Onion,
## HeavyCream,
## Shampoo} => {Bread} 0.01041667 0.6228571 0.01672401 1.230660 109inspect(sort(rules_bread, by = "support")[1:10])## lhs rhs support confidence coverage lift count
## [1] {} => {Bread} 0.5061162 0.5061162 1.0000000 1.000000 5296
## [2] {Salt} => {Bread} 0.2594610 0.5217141 0.4973242 1.030819 2715
## [3] {HeavyCream} => {Bread} 0.2553517 0.5144397 0.4963685 1.016446 2672
## [4] {Egg} => {Bread} 0.2550650 0.5130719 0.4971330 1.013743 2669
## [5] {Olive} => {Bread} 0.2545872 0.5088825 0.5002867 1.005466 2664
## [6] {Bacon} => {Bread} 0.2541093 0.5117398 0.4965596 1.011111 2659
## [7] {Butter} => {Bread} 0.2532492 0.5088326 0.4977064 1.005367 2650
## [8] {Onion} => {Bread} 0.2526758 0.5086572 0.4967508 1.005021 2644
## [9] {Milk} => {Bread} 0.2521024 0.5087753 0.4955084 1.005254 2638
## [10] {ShavingFoam} => {Bread} 0.2517202 0.5072213 0.4962729 1.002183 2634inspect(sort(rules_bread, by = "confidence")[1:10])## lhs rhs support confidence coverage lift count
## [1] {Bacon,
## Hazelnut,
## Carrot,
## Cucumber,
## Milk,
## Salt} => {Bread} 0.01032110 0.6625767 0.01557722 1.309139 108
## [2] {Honey,
## Hazelnut,
## Onion,
## HeavyCream,
## Egg,
## Shampoo} => {Bread} 0.01118119 0.6536313 0.01710627 1.291465 117
## [3] {Honey,
## Bacon,
## Hazelnut,
## Carrot,
## Milk,
## Salt} => {Bread} 0.01012997 0.6463415 0.01567278 1.277061 106
## [4] {Bacon,
## Hazelnut,
## Carrot,
## Cucumber,
## Salt,
## Shampoo} => {Bread} 0.01012997 0.6424242 0.01576835 1.269322 106
## [5] {Banana,
## Apple,
## Milk,
## ShavingFoam,
## Salt,
## Flour} => {Bread} 0.01051223 0.6395349 0.01643731 1.263613 110
## [6] {Honey,
## Hazelnut,
## Onion,
## Salt,
## HeavyCream,
## Shampoo} => {Bread} 0.01089450 0.6333333 0.01720183 1.251360 114
## [7] {Bacon,
## Apple,
## Hazelnut,
## Carrot,
## Cucumber,
## Salt} => {Bread} 0.01032110 0.6279070 0.01643731 1.240638 108
## [8] {Hazelnut,
## Onion,
## Milk,
## Salt,
## HeavyCream,
## Egg} => {Bread} 0.01012997 0.6272189 0.01615061 1.239278 106
## [9] {Honey,
## Banana,
## Hazelnut,
## Cheese,
## Cucumber,
## HeavyCream} => {Bread} 0.01041667 0.6228571 0.01672401 1.230660 109
## [10] {Honey,
## Hazelnut,
## Cucumber,
## Onion,
## HeavyCream,
## Shampoo} => {Bread} 0.01041667 0.6228571 0.01672401 1.230660 109Looking at the above tables there is clearly strong relation between bread and others products.
Conclusions
Bread is the most frequently purchased item, confirming its role as a staple in most households. The analysis also reveals that most baskets contain 11 products, indicating a common shopping pattern among customers. Through the Apriori algorithm, strong associations between various items have been uncovered, which can be used for effective cross-selling strategies. Additionally, the strongest rules suggest that bundling specific products together could enhance marketing strategies and increase sales.
To gain deeper insights, future research could explore different support and confidence thresholds to refine the association rules further. Analysing seasonal trends in product purchases may also reveal fluctuations in buying behavior throughout the year. Lastly, comparing this dataset with other retail datasets could provide a broader understanding of consumer purchasing habits across different markets.