ID3 and Apriori Algorithms

This assignment focuses on two crucial data mining algorithms: ID3 and Apriori. The solution explains the ID3 algorithm, a recursive method for building decision trees. It details how the algorithm selects a root node, splits data to create child nodes, and recursively processes attributes to classify unseen data. The solution then describes the Apriori algorithm, used for finding frequent itemsets in datasets. It outlines the algorithm's process of generating candidate itemsets and identifying frequent itemsets based on minimum support thresholds. The provided solution offers a clear explanation of both algorithms' functionalities and processes.

The ID3 algorithm is used to generate decision tree from the decision tree from
the dataset. It is a recursive algorithm and in this there is a root node and to move
further it splits the data to make its child node. If there is root node named as A
the algorithm made a decision attribute fir the node A for each value of the
attribute it gone to make its child node and whenever a new value for the
attribute comes first it will put the attribute in the particular subset of the node A
and then it checks the value of that attribute in all child node recursively. The
decision tree made by this algorithm is used to classify new unseen test cases by
working down the decision tree.
The apriori algorithm is influential algorithm and it is used to for the concept of
data mining. In this algorithm it made a subset of frequent item subset and the
entire element in the subset should be individual frequent item set. To discuss the
pseudo code of this algorithm Ck is the candidate item set of size k and Lk is the
frequent item set of size k. And then we run a loop from 1 to the size of frequent
item set maximum value and for each value of k increment the value of candidate
item set that is Ck value of Lk+1 becomes equal to value of Ck+1 with the
minimum support and after completing the loop return the value of Lk.