What is FP Growth Algorithm? A Comprehensive Guide

Data mining is an essential field of study that helps businesses and organizations to extract useful insights from large datasets. One of the most popular algorithms used in data mining is the frequent pattern growth (FP Growth) algorithm. The FP Growth algorithm is a powerful tool for discovering patterns and relationships within large datasets, making it an indispensable tool for many industries. The Understanding frequent pattern growth algorithm in data mining begins with understanding data science; you can get an insight of the same through our Data Science Training.   

FP Growth Algorithm (Frequent Pattern Growth Algorithm)

What if we didn't have to generate candidates but instead designed a mechanism that mined every possible group of often occurring items? Frequent-pattern growth (or FP-growth for short) is an intriguing approach in this effort that uses a divide-and-conquer strategy in the following way. First, it converts the database of frequently-used items into a frequent-pattern tree, or FP-tree, which keeps the association information between itemsets while compressing the data.The compressed database is then segmented into a group of conditional databases (a variant of the projected database) that are each tied to a single frequently occurring item or "pattern fragment," and these databases are mined independently, or we can say, the database is represented as a tree in the FP growth method, which is known as a frequent pattern tree.

In this way, the relationships between the sets of objects will be preserved in the tree structure. There is a lack of consistency in the database due to the presence of a single, common element. We refer to this shattered section as a "pattern fragment." These damaged  patterns' item sets are examined. The time spent looking for common collections of goods is thus decreased using this strategy.

FP TREE

A Frequent Pattern Tree is a tree-like structure constructed by using an initial database collection as its source material. The FP tree is used to determine which pattern occurs the most frequently. An FP tree can represent an itemset, with each node standing in for a particular object in the set. 

The empty set,symbolized by the root node, is partitioned into items by the succeeding nodes in the tree structure. The connections between the nodes, also known as item sets, and their subtrees, which consist of additional item sets, are maintained as the tree is being constructed.

Drawbacks of the Apriori Algorithm 

It has been demonstrated that the Apriori candidate generate-and-test approach may significantly reduce the size of candidate sets, resulting in a significant performance boost. There are, however, the potential for two significant drawbacks:

1. It might require a massive amount of candidate sets to be generated. If there are 104 standard  1-item sets, the Apriori algorithm will have to produce more than 107 new sets. Likely 2-item combinations. In addition, it has to produce at least 2100-11030 candidates in total in order to find a typical  pattern of size 100, such as {a1,..., a100}.
2. It might need many database searches and pattern-matching on a massive  number of potential candidates. Examining each transaction in the database to ascertain support for the potential itemsets is time-consuming and resource-intensive.

Why Do We Need Fp Growth Algorithm in Data Mining?

The FP-growth algorithm is a robust and effective technique for data mining that efficiently uncovers frequent patterns and associations in large datasets. It surpasses other frequent pattern mining algorithms like Apriori by compressing data into a compact data structure known as FP-tree, which allows for faster processing and decreased memory requirements, making it perfect for mining extensive datasets. The advantage of FP-growth is that it eliminates the time-consuming process of generating candidate itemsets, making it more useful for market basket analysis, web log analysis, and bioinformatics. It's a valuable tool in making informed decisions where identifying frequent patterns in large datasets is critical.

Steps to Frequent Pattern Growth Algorithm in Data Mining 

1. The first step is to do a database search for all instances of the item sets. This is equivalent to the first stage of the Apriori method. The number of 1-item sets  that exist in the database is referred to as the support count or the frequency of a 1-item set.
2. The second step is to construct the FP tree. For this, create the root of the tree. The root is represented by null.
3. The next thing to do is to run another database scan and look at the transactions. The initial transaction should be analyzed  to determine what set of items was changed. Take the set with the highest count first, then the next lowest count, and so on. That section of the tree is built from sets of transactions that decrease in size from top to bottom.
4. The next logical database transaction is reviewed. The collections are arranged from the least to the greatest number of things. This transaction branch would share a common prefix with  the root if any of its itemsets also existed in another branch (for example, in the 1st transaction). This means that the common itemset is linked to the new node of another itemset in this transaction.
5. As a result of the transactions, the set  count grows in real-time . As new nodes are added to the network and existing ones are connected, the total number of nodes in the system grows by 1.
6. The following step is to mine the FP Tree that was just generated. In order to do this, the node with the fewest links is investigated first, along with the lowest node itself and its linkages. The frequency pattern length 1 is represented by the lowest node . Proceed down the route in the FP Tree that begins here. One or more of these routes is called  a conditional pattern basis.A sub-database known as the conditional pattern base comprises  prefix pathways in the FP tree that begin with the lowest node (suffix).
7. Make a Conditional FP Tree by adding up all the sets of things along the route. The Conditional FP Tree only takes into account the sets of items that receive sufficient support to pass the threshold.
8. Frequent Patterns are generated from the Conditional FP Tree.

Frequent Pattern Growth Algorithm Example In Data Mining 

To  fully understand the FP-TREE method, let's examine the sample.

Example : 

Take this as an example of a supermarket dataset, in which we have information on five  separate item purchases.

[‘Milk,’ ‘Onion,’ ‘Nutmeg,’ ‘Kidney Beans,’ ‘Eggs,’ ‘Yogurt’],

[‘Dill,’ ‘Onion,’ ‘Nutmeg,’ ‘Kidney Beans,’ ‘Eggs,’ ‘Yogurt’],

[‘Milk,’ ‘Apple,’ ‘Kidney Beans,’ 'Eggs’],

[‘Milk,’ ‘Unicorn,’ ‘Corn,’ ‘Kidney Beans,’ ‘Yogurt’],

[‘Corn,’ ‘Onion,’ ‘Onion,’ ‘Ice cream,’ ‘Eggs’]

Step 1: The frequency with which an item occurs affects how much support it receives. Here, we're talking about an item-wide minimum support of 3 or 0.6.

Step 2: we can observe that kidney beans appear in every transaction; this means that the structure will have five supports by the time we reach the last one but only one in the very first one. We then reorder the items in the dataset based on the strength of their support.

[‘Kidney Beans,’ ‘Eggs,’ ‘Yogurt,’ ‘Onion,’ ‘Milk’],

[‘Kidney Beans,’ ‘Eggs,’ ‘Yogurt,’ ‘Onion’],

[‘Kidney Beans,’ ‘Eggs,’ ‘Milk’],

[‘Kidney Beans,’ ‘Yogurt,’ ‘Milk’],

[‘Eggs,’ ‘Onion’]

We have determined that either a degree of support of 3 or 0.6 is appropriate for this data set. 

Step 3: Create a chart like the one shown and add the first data transaction to it:

The results of step 2 show that there are records for these things and items with support degree 3 and more than three in the first transaction and across the whole dataset.

Our tree can be enhanced for the second exchange (below image). Since milk was unavailable in the second transaction, we can see that the total amount of help has increased by one value for all other items.

Furthermore, we may enhance the tree for all transactions; the figure below shows the tree after enhancing the third transaction.

The FP-tree will look like this in the final visualization .

The FP-tree algorithm's degree of support counting is revealed in the last stage. Frequent sets are shown by the curved line, and the beginning of each transaction is shown by the straight line, or the branch, in the tree metaphor. All items with fewer than three support degrees are eliminated.

The next step is to design an FP-tree data structure and implement an FP-growth algorithm to help us decide between alternatives or suggest complementary products.

You can learn more about the six stages of data science processing to better grasp the above topic. 

Advantages of FP Growth Algorithm 

1. In contrast to Apriori, which does a database search for each iteration, this method only needs to do so twice.
2. This method is quicker since it skips the step of matching objects together.
3. A condensed version of the database is kept in RAM.
4. It mines long and short frequent patterns with equal efficiency and scalability.

Problems with the FP-Growth Algorithm

1. Compared to Apriori, constructing an FP Tree is more time-consuming and complicated.
2. It might end up costing quite a bit.
3. In the case of a sizable database, the algorithm may outgrow the available shared memory.

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Conclusion

The Apriori algorithm is utilized in the mining of various sorts of association rules in order to get the desired results. The method can  effectively find groupings of items that are often used if it is based on the concept that "the non-empty subsets of frequent itemsets must also be frequent.A database scan is performed in order to determine the item combinations that occur the most frequently, and candidates for k-itemsets are created from itemsets that have a size of (k-1).

Eliminating the need to produce candidates is a side effect of using the Frequent Pattern Growth Algorithm to locate common patterns. An FP Tree is constructed here as an alternative to Apriori's development  and test technique. Mining common patterns and dividing up the paths taken by objects are two of the primary focuses of the FP Growth algorithm. You can check out the data science certification guide, provided by JanBask training to understand more about the skills and expertise that can help you boost your career in data science and data transformation in data mining.