Artificial Intelligence vs Business Intelligence

It starts from the root node, explores neighbor nodes first, and moves to the next-level neighbor nodes. It provides the shortest path to the solution and can be implemented using FIFO.

This extracts the predictive information as human-understandable rules, which can be if-then-else, leading to the predictive information.

This algorithm uses LIFO(Last in, first out)data structure. It creates nodes the same as breadth-first search but differs in only order. In each iteration, it stores the nodes from root to leaf and cannot check duplicate nodes.

It makes predictions using the Bayes algorithm, which derives probability prediction from the underlying evidence, as observed in data.

In this algorithm, sorting is done by increasing the path’s cost to a node. It continuously expands the least-cost node. This search is identical to the Breadth-first search if each transition has the same cost. It explores the path in the increasing order of cost.

It implements logistic regression for the classification of binary targets and linear regression for continuous targets. It supports confidence bounds for prediction probabilities and confidence bounds for prediction.

It performs the depth-first search at level-1, starts over, then executes a complete depth-first search to level 2 and continues until it gets the solution.

It is an information-theoretic model selection principle. It assumes that the most simple, compact data representation is the best way to explain it.

It expands nodes in the order of their Heuristic values. It creates two lists, a closed list for the already developed nodes and an open list for the created but unexpanded nodes. The shorter paths are saved, and longer paths are disposed of.

A distance-based clustering algorithm partitions the data into a pre-determined number of clusters. Each cluster has a centroid.

In this algorithm, the main aim is to find a low-cost tour that starts from a city, visits all cities en route exactly once, and ends at the same city starting.

It performs market-based analysis by discovering co-occurring items within a set. This algorithm finds rules with support greater than a specified minimum support and confidence greater than a specified minimum confidence.

An iterative algorithm starts with an arbitrary solution to a problem and attempts to find a better solution by incrementally changing a single solution element. If that change produces a better solution, an incremental change is taken as a new solution. This process is repeated until there are no further improvements.

Distinct versions of SVM use different kernel functions to handle different types of data sets. Linear and Gaussian(non-linear) kernels are supported.SVM classification attempts to separate the target classes with the widest possible margin.SVM regression tries to find a continuous function such that the maximum number of data points lie within an epsilon-wide tube around it.