All New SEO Apriori Algorithm and here’s what you need to know

All New SEO Apriori Algorithm and here’s what you need to know
Importance of Apriori Algorithm for SEO

The web is an enormous information space having a large number of individual articles like documents, images, videos or other multimedia that could be retrieved. In this context, several information technologies have been developed that users to gratify their search needs and the most popular of them are search engines, such as Yahoo, Google, Netscape, e-Bay, e-Trade, Expedia, Amazon, Bing, Ask, and many more.

Evolution of Apriori Algorithm

The search engines review a list of answers allowing users to find web-relevant resources by setting up their queries. The proposed method starts by exploring the query logs to identify the session of queries and then examines query logs to discover the useful relationships among pages and keywords using the algorithm of association rule mining like that of the apriori algorithm. One of the biggest challenges an SEO faces is maintaining focus in a world of data with disparate tools that do various things well.

We have huge data coming out, but the main thing is how to refine it to something meaningful. As we SEOs do all the time, we mix new with old to create a tool that has value for something. We will leverage a little-known algorithm called the Apriori Algorithm in python to produce a useful workflow for understanding your organic visibility. As compared to the apriori algorithm, the automated apriori algorithm generates more strong rules that too will be discussed here.

The Apriori algorithm was first proposed by Rakesh Agrawal and Ramakrishnan Srikant in 1994 to find associations/commonalities between parts of rows of data, called transactions, as a fast efficient algorithm used on large databases. Apriori Algorithm is the most commonly used association rule finding algorithm that searches the frequent items set strategy, which works well when used on a large scale of data set to find the frequent items.

Based on association rules construct from query log the method provides query recommendation,  query reformulation, and improved page ranking. To understands Apriori Algorithm we must first understand the term data mining or web mining. Web mining is the combined term of various techniques such as clustering,  classification,  and association to automatically find and extract needful information.

It is a  forced area from many research communities which includes large databases,  IR,  artificial intelligence, and statistics.  The subset of any frequent itemsets must also be frequent, which is the key Principal of the Apriori Algorithm. 

Working Mechanism of the Algorithm

Here are some of the steps associated with the Apriori Algorithm. On the first basis algorithm simply counts item occurrences to determine frequent itemsets, which means all the singleton items are included and items having less support value then the threshold are eliminated from the list of candidate items. In the next step, the singleton item is combined to form two sets of candidates item and the support values of these candidates are again scanned to determine the data sets.

In this step, the candidates with support value higher than the threshold are only considered and the items eliminated in the first pass are not considered again. Now algorithm creates three-member candidate itemsets till all frequent itemsets are accounted. In the fourth step, itemsets are used to generate association rules having confidence values greater than the threshold.

Firstly the rules of frequent itemsets are created which is followed by the creation of subsets. Based on the support and the confidence thresholds an interesting relationship between the items in the database is discovered using association rules for data creation. As compared to the apriori algorithm more strong rules with cumulative support are generated by automated Apriori algorithm. Here are some of the steps associated with the Automated Apriori Algorithm.

Firstly calculate the support of each item and arrange them in ascending order according to their support. Now calculate ms of each item and generate all frequent itemsets. Calculate cumulative support and mini support for each item sets. Now select frequent itemsets and generate strong association rules from frequent itemsets. Apriori Algorithm proceeds by identifying the frequent individual items in the database and extend the larger item sets until it starts appearing in the database regularly.

This method is very helpful in determining association rules showing general trends in the database which could be easily applied in domains such as market basket analysis. Apriori uses a “bottom-up” approach extending frequent subsets with one item at a time testing a group of candidates against the data. The algorithm automatically terminates when no further extensions are found.

Link with SEO

Let us understand the concept of data mining and its close association with the Apriori Algorithm with a very practical example based on the study. A salesperson from Wal-Mart bundled the products together giving interesting discounts to increase sales. He bundled bread and jam which are frequently used together and customers could buy them because of discounts. To find some more opportunities the sales guy analyzed all sales records.

He found an interesting trend that customers who purchased diapers also bought beers. He decided to study the trend as the two products are unrelated. He found that raising kids is gruelling, so to stay away from stress parents decided to buy beer. He paired diapers with beers and as expected the sales escalated. Now, in the technical form, you can call it Association Rules in data mining.

Apriori algorithm is a classical algorithm in data mining used for mining frequent itemsets and relevant association rules. With the quick growth in e-commerce applications, a vast quantity of data is accumulated in months. Apriori Algorithm can determine the anomalies, correlations, patterns, and trends to predict the possible outcomes. It is devised to operate on a database containing a lot of transactions like items brought by customers in a store or on an e-commerce website. It helps in increasing the sales of the market by making an effective Market Basket Analysis helping customers in purchasing their items with more ease. It has also been used in the field of healthcare in producing association rules indicating what combination of medications leads to adverse drug reactions.

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