Are you leveraging Association Rule Learning!
This blog aims to explain what is Association Rule Learning in-depth, its application in the product-based business world to gain huge benefits & how it can be differentiated from Recommender systems!
The fact that technologies like Data Science (DS), Machine Learning (ML), Artificial Intelligence (AI), & all other similar technologies are hot topics in today’s world is known by everyone. But, the challenge lies in knowing the subfields & their use-cases.
The majority of the tech guys in the world are looking forward to learning Machine Learning today, the very first challenge here is that everyone is just trying to learn ML, nobody is trying to understand it. The second challenge is that the approach towards this ML is not correct, because everyone just tries to go with the simple algorithms or the famous algorithms that are present in the market.
The right approach towards ML will be, first of all, explore in which ways it can be implemented, where it can be implemented. When someone tries to go with this approach, then that person will find a huge application (discussed further in this blog) of ML in all the product-based businesses using which businesses can earn a lot of money & they are already earning. This application is possible through a subfield of ML that is Association Rule Learning (ARL).
Now, that being said, let’s first discuss the scenario in the product-based business world on top of which ARL can be applied to get huge benefits.
The scenario in all the Product-based Business World that needs to be solved!
Take an example of a very simple business i.e., grocery store. Whenever a person visits a store, most of the time, that person will shop for more than 1 item. Now, the point is, if the owner of the grocery store get some insights like “If a person is buying a particular product, then the same person will buy which other product?”, then the owner can create a deal in a way that it can give the second product as complimentary, obviously, creating the deal (setting the price of both the products as one) in a way that will benefit everyone.
For example:
- If a person buys a white sauce or red sauce, then the person will also buy pasta.
- If a person is buying soya sauce, then the person will buy noodles also with the soya sauce.
- If a person is buying bread, it will buy butter also or jam with bread.
Using subfield of ML to get the insights like mentioned above!
In ML, there is a field known as Association Rule Learning (ARL), that helps to achieve the insights as mentioned in the above examples.
The goal of ARL is to find the linkage or dependency between 2 or more products/items/entities. This goal can be accomplished by implementing different logics that are implemented by different algorithms.
The most famous algorithms to perform ARL are:
- Apriori
- Eclat
I will publish blogs covering both the above algorithms in detail in the future, Once I publish them, I will update their links here!
Difference between Association Rule Learning & Recommendation Systems!
Although both the terms appear to be exactly equal, however, if you think logically, you will find a small difference between the two, & that is, ARL focuses on finding the dependency or linkage between the items, whereas recommendation systems try to find the interest of a particular user in another entity based on the taste/interest of the user!
I hope my article explains each and everything related to the topic with all the detailed concepts and explanations. Thank you so much for investing your time in reading my blog & boosting your knowledge. If you like my work, then I request you to applaud this blog & follow me on Medium, GitHub, & LinkedIn for the more amazing content on multiple technologies and their integration!
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