.Net Conf Singapore 2019 - Overview On Apriori Machine Learning Algorithm
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The document contains data from market basket analysis, listing items purchased together and their support levels. It shows individual item support levels, support for pairs of items, and the confidence levels of association rules between items. It also includes links to blog posts explaining market basket analysis and the Apriori algorithm used for association rule mining.
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.Net Conf Singapore 2019 - Overview On Apriori Machine Learning Algorithm
- 12. TID Items 1 Apple, Orange, Lime 2 Orange, Lime, Strawberries, Banana 3 Blueberries, Lime, Banana, Orange 4 Grapes, Orange, Banana 5 Apple, Blueberries, Raspberries, Banana 6 Orange, Apple, Banana 7 Lime, Orange, Apple, Banana 8 Avocado, Strawberries, Orange 9 Orange, Lime, Banana 10 Apple, Banana, Orange, Lime
- 13. Itemsets Support Apple 0.5 Orange 0.9 Lime 0.6 Strawberries 0.2 Blueberries 0.2 Banana 0.8 Raspberries 0.1 Grapes 0.1 Avocado 0.1
- 14. Itemsets Support Apple, Orange 0.4 Apple, Banana 0.4 Apple, Lime 0.3 Orange, Banana 0.7 Orange, Lime 0.6 Banana, Lime 0.5
- 15. Itemsets Support Banana, Orange, Lime 0.5
- 16. X Y Support of X Support of X+Y Confidence Orange Banana 0.9 0.7 0.77 Orange Lime 0.9 0.6 0.66 Banana Lime 0.8 0.5 0.62 Banana Orange, Lime 0.8 0.5 0.62 Orange Banana, Lime 0.9 0.5 0.55 Lime Banana, Orange 0.6 0.5 0.83 Banana, Orange Lime 0.7 0.5 0.71 Orange, Lime Banana 0.6 0.5 0.83 Banana, Lime Orange 0.5 0.5 1
Editor's Notes
- Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. They try to find out associations between different items and products that can be sold together, which gives assisting in right product placement. Typically, it figures out what products are being bought together and organizations can place products in a similar manner. Let’s understand this better with an example: People who buy Bread usually buy Butter too. The Marketing teams at retail stores should target customers who buy bread and butter and provide an offer to them so that they buy the third item, like eggs. So if customers buy bread and butter and see a discount or an offer on eggs, they will be encouraged to spend more and buy the eggs. This is what market basket analysis is all about.
- People who buy Bread usually buy Butter too. The Marketing teams at retail stores should target customers who buy bread and butter and provide an offer to them so that they buy the third item, like eggs. So if customers buy bread and butter and see a discount or an offer on eggs, they will be encouraged to spend more and buy the eggs. This is what market basket analysis is all about.
- Three significant components comprise the apriori algorithm. They are as follows. Support Confidence Lift
- Measure 1: Support. This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears. In Table 1 below, the support of {apple} is 4 out of 8, or 50%. Itemsets can also contain multiple items. For instance, the support of {apple, beer, rice} is 2 out of 8, or 25%. If you discover that sales of items beyond a certain proportion tend to have a significant impact on your profits, you might consider using that proportion as your support threshold. You may then identify itemsets with support values above this threshold as significant itemsets.
- Measure 2: Confidence. This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears. In Table 1, the confidence of {apple -> beer} is 3 out of 4, or 75%. One drawback of the confidence measure is that it might misrepresent the importance of an association. This is because it only accounts for how popular apples are, but not beers. If beers are also very popular in general, there will be a higher chance that a transaction containing apples will also contain beers, thus inflating the confidence measure. To account for the base popularity of both constituent items, we use a third measure called lift.
- This says how likely item Y is purchased when item X is purchased, while controlling for how popular item Y is. In Table 1, the lift of {apple -> beer} is 1,which implies no association between items. A lift value greater than 1 means that item Y is likely to be bought if item X is bought, while a value less than 1 means that item Y is unlikely to be bought if item X is bought.
- Computationally Expensive. Even though the apriori algorithm reduces the number of candidate itemsets to consider, this number could still be huge when store inventories are large or when the support threshold is low. However, an alternative solution would be to reduce the number of comparisons by using advanced data structures, such as hash tables, to sort candidate itemsets more efficiently. Spurious Associations. Analysis of large inventories would involve more itemset configurations, and the support threshold might have to be lowered to detect certain associations. However, lowering the support threshold might also increase the number of spurious associations detected. To ensure that identified associations are generalizable, they could first be distilled from a training dataset, before having their support and confidence assessed in a separate test dataset.
- Let’s take for example the following list of transactions:
- In this example, our minimum support is 0.5 and our minimum confidence 0.5. Let’s extract the 1-itemsets from it and calculate their support: As you can see, a lot of itemsets were removed because they don’t meet the minimum support threshold.
- Now let’s extract the 2-itemsets using the 1-itemsets and calculate their support: We have less elements to consider at each step, which reduces the number of supports we need to calculate.
- Now to our 3-itemsets: Finally, we’re left with one 3-itemset that happens to be frequent. Thanks to the Apriori algorithm, we ended up checking 16 candidate itemsets instead of many more.
- The next and last step is creating the association rules from all the frequent itemsets that have at least 2 items and calculate their confidence. Luckily, all our association rules met our minimum confidence threshold! If you noticed, we got an association rule with a confidence equal to 1. This means that WHENEVER we see its X, the Y will be there in the transactions. If we check to make sure, all the transactions containing a Banana and a Lime contain indeed an Orange.