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Sai Kiran Chintalapudi
Harshavardhan Metla
Yeshwanth Kamisetty
Nalluri Rahul
Manikandan K
Siddharth Kolagatla


A popular Association Rule Mining algorithm called Apriori algorithm helps in finding various frequent itemsets in the database. The constraints for finding these itemsets are given by the user in terms of support - measured by the proportion of transactions in which an itemset appears, and confidence - measured by the proportion of transactions with an itemset, in which another itemset also appears. The problem with this algorithm is that it is highly iterative and thus its efficiency rapidly decreases with increase in size or dimension of the dataset. Our project increases its efficiency with the help of openMP threads. We use data decomposition to split the transaction database into various parts, each taken by a thread to find the support count of all the candidate itemsets for all the transactions assigned to that particular thread. To give an example of the application, this project is used to determine the probability of the occurrence of a forest fire. Here, the transaction database can consist of various occurrences of natural phenomena, in which a few transactions also have the forest fire phenomenon, which means that it has occurred in the presence of the other itemsets in the transaction. Hence, if a new transaction is taken from the user, then the probability (or confidence) that a forest fire occurs, given this transaction, is calculated.


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