Fatemeh Hajiloo

Bio: Fatemeh Hajiloo is an academic researcher from University of Southern California. The author has contributed to research in topics: Association rule learning. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

An Analysis on Characteristics of Negative Association Rules

Abstract: Association rules are one of the data and web mining techniques which aim to discover the frequent patterns among itemsets in a transactional database. Frequent patterns and correlation between itemsets in datasets and databases are extracted by these interesting rules. The association rules are positive or negative, and each has its own specific characteristics and definitions. The mentioned algorithms of the discovery of association rules are always facing challenges, including the extraction of only positive rules, while negative rules in databases are also important for a manager’s decision making. Also, the threshold level for support and confidence criteria is always manual with trial and error by the user and the proper place or the characteristics of datasets is not clear for these rules. This research analyses the behavior of the negative association rules based on trial and error. After analyzing the available algorithms, the most efficient algorithm is implemented and then the negative rules are extracted. This test repeats on several standard datasets to evaluate the behavior of the negative rules. The analyses of the achieved outputs reveal that some of the interesting patterns are detected by the negative rules, while the positive rules could not detect such helpful rules. This study emphasizes that extracting only positive rules for covering association rules is not enough.

An Extension of the Apriori Algorithm for Finding Frequent Items

Abstract: The main purpose of data mining is to discover hidden and valuable knowledge from data The Apriori algorithm is inefficient due to bulky deals of searching in a dataset Bearing this in mind, this paper proposes an improved algorithm from Apriori using an intelligent method Proposing an intelligent method in this study is to fulfill two purposes: First, we demonstrated that to create itemsets, instead of adding one item at each step, several items could be added With this operation, the number of k-itemset steps will decline Secondly, we have proved that by storing the transaction number of each itemset, there would be a diminishment in the time required for the dataset searches to find the frequent k-itemset in each step To evaluate the performance, the Intelligent Apriori (lAP) algorithm has been compared with the MDC algorithm The results of this experiment exhibit that since the transaction scans used to obtain the itemset momentously reduced in number, there was a considerable fall in the runtime needed to obtain a frequent itemset by the proposed algorithm In this study, the time required to generate frequent items had a 46% reduction compared to that of the MDC_ Apriori algorithm