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What is Apriori Algorithm in Data Mining?
Table of Contents
In this age of advancement where globalization and other e-commerce retail shops are becoming famous, there it becomes imperative for various businesses to use machine learning and artificial intelligence to stay ahead of the competition. Hence, data analysis and computer science have an enormous scope in today's environment to cope up with dynamic competitions. There are various methods of data mining in computer language, which are mostly association, correlation, and clustering, etc.
What is Apriori Algorithm?
Apriori Algorithm usually contains or deals with a large number of transactions. For example, customers buying a lot of goods from a grocery store, by applying this method of the algorithm the grocery stores can enhance their sales performance and could work effectively. It is also very effective in the field of healthcare for the detection of adverse drug reactions. To perform this algorithm, one should know about association rules because it is the most important and well-explored method for knowing the weak or the strong relationships among variables in a huge database and information. Apriori Algorithm has the property that helps to improve the efficiency level by reducing the search space.
Basic Terms Used in Apriori Algorithm
Before we know more about the Apriori algorithm, we should know about the basic terms used in this algorithm, and that is:
Data mining: Refers To the process of finding information from a large database.
Frequent itemsets: It refers to all those sets which contain the item with the minimum support, and it is usually denoted by Li for ith item set.
Apriori property: Frequent set and subsets used.
Join operation: Finding the outset of candidates.
Frequent mining pattern: Regular data mining applied through the algorithm
Application of Apriori Algorithm in the Real World
1. Education Field
As we all know that all the students studying in the school have different characteristics and personalities like every student are different in age, gender, different names, and different parents' names, etc.
2. Medical Field
In every hospital, there are a lot of patients admitted over there every patient have a different kind of disease according to which they are given a different treatment, and they all will have a different type of characteristics and different medical history, so here it is necessary to use the computer science method of Apriori algorithm in order analyze the patients' database. So that there should be no mixing of the information of different patients
In every forest, there are several kinds of flora and fauna. And, naturally, there are a variety of trees which are having different features like their sizes, texture, seasons of trees, etc. similarly, there are various types of animals having a variety of features, which makes them different from one another. So to analyze all such details, we use the method of the Apriori algorithm.
4. New Technology Business Firms
Apriori is used by many companies like Flipkart, Amazon, etc. where they have to maintain the record of various items of products that are purchased by various customers for recommender systems and by google for the autocomplete features.
One of the most efficient uses of this computer science technique is in the offices where they have to record a large number of day to day transactions related to sale and purchase of various good and services, like recording the transactions of creditors, sales and purchases so there is need of analysis of all such transactions so that there should not be any kind of confusion.
6. Mobile e-Commerce
The main purpose of using this technique is to make mobile e-commerce shopping easy, convenient, and increase the customer base crossing national and international borders. The deployment of the unity of real-time and recommendation accuracy is made so that mobile e-commerce gets the maximum benefit of the Apriori algorithm.
Difference between Classical and Advanced Apriori Algorithm
- In the classical Apriori algorithm, when candidate itemsets are generated, the algorithm needs to test their occurrence frequencies because of which it requires a lot of time wastage, but in advanced Apriori algorithm it takes very less time
- Database size in classical Apriori algorithm is very large, but it is very small in the improved version of the Apriori algorithm
- The database scan in the earlier Apriori algorithm is N times on different database servers. But in advanced technique, it is n times.
- Advanced mode is more efficient than that of the classical Apriori algorithm because it uses less wastage of time and space.
- Memory requirement in the advanced model of the Apriori algorithm is more than the classical one
- Speed of performing work is very high than that of the classical one
Discussion on Mining Association Rules
The mining association rules are explained with the help of the two-step approach here :
Step 1: Frequent Itemset Generation
Find all itemsets for which the support is greater than the threshold support following the process w
Step2: Rule Generation
Create rules for each frequent itemset using the binary partition of frequent itemsets and look for the one with high confidence.
Example of Apriori Algorithm
Consider the following database of a grocery store:
Items of database
Here we will try to appear at least three transactions with the value of 3, which will support the threshold.
By scanning the database for the first time, we obtain the following result
All the itemsets of the size 1 have the support of at least 3, so they are all frequent:
The pairs(1,2),(2,3),(2,4), and(3,4) all meet or exceed the minimum support of 3, so they are frequent.
The pairs(1,3)and (1,4)are not. Now because (1,3) and (1,4) are not frequent, any large set which contains (1,3)or(1,4) can not be frequent.
We have seen how you can deduce various kinds of data and enhance the sales performance of the supermarket and grocery stores. That was one example of the utility of the Apriori algorithm. Not only in supermarkets, but this concept is widely used in other critical industries like healthcare industries, and so on. It enables the industry to bundle drugs that cause the least ADRS depending on the patient's characteristics. Although it is a time-consuming process still makes work easier wherever there is the involvement of a large database.
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