Artificial intelligence as a field and a practice is based on algorithms. Those functions allow input to become output. Tracking and tweaking inputs allow machines to learn and accomplish amazing feats. However, constant focus on inputs and machines can distort the focus of other, equally important aspects of the artificial intelligence process. One of these is the algorithm. While sometimes ignored, algorithms such as the Apriori algorithm mean the difference between a failed artificial intelligence process and one that quickly sorts and analyzes massive reams of data.
The Apriori algorithm is a categorization algorithm. Some algorithms are used to create binary appraisals of information or find a regression relationship. Others are used to predict trends and patterns that are originally identified. Apriori is a basic machine learning algorithm which is used to sort information into categories. Sorting information can be incredibly helpful with any data management process. It ensures that data users are appraised of new information and can figure out the data that they are working with.
Apriori has a wide variety of applicable datasets. A table with only a handful of entries can still use Apriori to make sense of available data. However, machine learning is only preferable to human learning when it works at a scale and complexity that human beings cannot easily master. As a result, Apriori often works with a large data set. These data sets may include thousands of entries of either qualitative or quantitative data.
Data is most often organized into some sort of database or table. However, such organization is not absolutely necessary for the machine learning algorithm to do its work. The data may be entered into an artificial neural network or another form of artificial intelligence. Either way, it must be present with guiding information such as timestamps or dates. Guiding information helps the machine learning algorithm process categories and find patterns. A simple list of numbers could only be sorted on the basis of general amount or frequency.
This machine learning algorithm works by identifying a particular characteristic of a data set and attempting to note how frequently that characteristic pops up throughout the set. This idea requires some extra work on the part of the person implementing the design and, later, the machine itself. The definition of “frequent” is inherently relative and only makes sense in context. Therefore, the idea is implemented in the Apriori algorithm through a pre-arranged amount determined by either the operator or the algorithm. A “frequent” data characteristic is one that occurs above that pre-arranged amount, known as a support.
The characteristics that are frequent can then be analyzed and place into pairs. This process helps to point out more relationships between relevant data points. Other forms of data can be pruned and placed into their own categories. Pruning helps to further differentiate between categories that do and do not reach the overall support amount. Next, the data set can be analyzed by looking for triplets. These triplets show even greater frequency. Analysis can detect more and more relations throughout the body of data until the algorithm has exhausted all of the possible.
The Apriori algorithm can be used under conditions of both supervised and unsupervised learning. In supervised learning, the algorithm works with a basic example set. It runs the algorithm again and again with different weights on certain factors. The desired outcome is a particular data set and series of categories. Once the algorithm can place frequent characteristics into particular datasets, it is then able to analyze new areas of data. Categories and relationships are key. Apriori must be able to properly categorize and label pieces of data.
Unsupervised learning is less structured and connected closer to relationships. There is no example dataset or predetermined approach to identifying patterns and relationships. Instead, there is simply a series of guidelines. These guidelines may include desired outcomes or a set of data points that the algorithm should focus frequency identification on. However, the focus is primarily on finding new patterns and frequent pieces of data. By pouring over thousands of data points and identifying patterns and points that appear frequently, the Apriori algorithm is able to create new categories and bring up new ideas.
Apriori is mainly used for sorting large amounts of data. Sorting data often occurs because of association rules. Rules help show what aspects of data different sets have in common. Categories can then be built around those association rules. With data in categories, algorithms and users can spot new trends and structure data sets. They may have a better ability to point out trends over time. The algorithm can also be used to track how relationships develop and categories are built.
Apriori can be used as a basis for an artificial neural network. It can help the network make sense of large reams of data and sort data into categories by frequency almost instantaneously. An artificial neural network using Apriori can also tweak the weighting on different categories to expand or diminish the importance of those categories. As a result, an artificial neural network can process data, identify trends, and elaborate on patterns that would otherwise be missed. Apriori is incredibly helpful for data analysts in numerous fields. Its importance will only continue to grow as more and more fields use artificial intelligence to make sense of massive data sets. Apriori will continue to be an essential tool in the growth of machine learning and artificial intelligence for years to come.