Artificial Intelligence and Business Intelligence: Differences and Comparisons

Artificial Intelligence Algorithms

Business Intelligence Algorithms

Algorithm for breadth-first searches

Beginning with the root node, it first investigates the neighbor nodes before moving on to the neighbors at the next level. It offers the quickest route to the answer and can be used with FIFO.

Decision Tree Algorithm

The predictive information is extracted in the form of rules that are intelligible by humans, and these rules may be if-then-else rules.

Depth First Search Algorithm

Utilizing the LIFO (Last in First Out) data structure, this procedure is carried out. Similar to breadth-first search, it builds nodes, but the only difference is in the order. It saves the nodes from root to leaf throughout each iteration and is unable to check for duplicate nodes.

Naive Bayes

Using the Bayes algorithm, which derives probability forecasts from the underlying evidence as shown in the data, it provides predictions.

Uniform Cost Search Algorithm

By raising the cost of the path to a node, this algorithm sorts data. The least expensive node is always expanded. If each transition costs the same amount, this search is the same as the breadth-first search. It investigates the route in the ascending order of expense.

Generalized Linear models,

 uses linear regression for continuous targets and logistic regression to classify binary targets. Both confidence constraints for predictions and bounds for prediction probabilities are supported.

Iterative Deepening Depth-first Search

When it reaches level 1, it restarts the depth-first search and moves on to level 2, where it repeats the process until it finds the answer.

Minimum Description Length

It is a model selection principle based on information theory. It makes the supposition that the best method to explain data is to offer it in the simplest, most concise manner possible.

Pure Heuristic Search

Nodes are expanded according to their Heuristic values. It generates two lists: an open list for newly produced but unexpanded nodes and a closed list for nodes that have previously been expanded. This discards the longer pathways while saving the shorter ones.

K-Means Algorithm

The program divides the data into a predetermined number of clusters using a distance-based approach. There is a centroid in each cluster.

Travelling Salesman Problem

The basic goal of this algorithm is to locate a cheap tour that departs from a city and stops in each city along the way exactly once, then returns to the original starting location.

Apriori Algorithm

By identifying co-occurring items within a set, it does market-based analysis. This algorithm looks for rules that have more support and confidence than minimal support and minimum confidence, respectively.

Hill-climbing search

is an iterative method that begins with a randomly chosen solution to a problem and seeks to improve upon it by gradually modifying each component of the solution. An incremental modification is regarded as a new solution if it results in a superior one. Until no more advancements are made, this process is repeated.

Support Vector Machine

Different iterations of SVM handle various kinds of data sets using various kernel functions. Both linear and non-linear Gaussian kernels can be used. With SVM classification, the target classes are separated by as much of a margin as possible. The goal of SVM regression is to identify a continuous function that has the most data points within an epsilon-wide tube surrounding it.