Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Article Citation - WoS: 1Citation - Scopus: 3Ignoring Internal Utilities in High-Utility Itemset Mining(MDPI, 2022) Oğuz, DamlaHigh-utility itemset mining discovers a set of items that are sold together and have utility values higher than a given minimum utility threshold. The utilities of these itemsets are calculated by considering their internal and external utility values, which correspond, respectively, to the quantity sold of each item in each transaction and profit units. Therefore, internal and external utilities have symmetric effects on deciding whether an itemset is high-utility. The symmetric contributions of both utilities cause two major related challenges. First, itemsets with low external utility values can easily exceed the minimum utility threshold if they are sold extensively. In this case, such itemsets can be found more efficiently using frequent itemset mining. Second, a large number of high-utility itemsets are generated, which can result in interesting or important high-utility itemsets that are overlooked. This study presents an asymmetric approach in which the internal utility values are ignored when finding high-utility itemsets with high external utility values. The experimental results of two real datasets reveal that the external utility values have fundamental effects on the high-utility itemsets. The results of this study also show that this effect tends to increase for high values of the minimum utility threshold. Moreover, the proposed approach reduces the execution time.Article Citation - WoS: 1Citation - Scopus: 1Minimizing Information Loss in Shared Data: Hiding Frequent Patterns With Multiple Sensitive Support Thresholds(Wiley, 2020) Bostanoğlu, Belgin Ergenç; Öztürk, Ahmet CumhurPrivacy preserving data mining (PPDM) is the process of protecting sensitive knowledge from being discovered by data mining techniques in case of data sharing. Privacy preserving frequent itemset mining (PPFIM) is a subtask and NP-hard problem of PPDM. Its objective is to modify a given database in such a way that none of the sensitive itemsets of the database owner can be obtained by any frequent itemset mining technique from the modified database. The main challenge of PPFIM is to minimize the distortion given to the data and nonsensitive knowledge while sanitizing all given sensitive itemsets. Distortion-based sensitive itemset hiding algorithms decrease the support of each sensitive itemset under a predefined sensitive threshold through sanitization. Most of the distortion-based itemset hiding algorithms allow database owner to define a single sensitive threshold for each sensitive itemset. However, this is a limitation to the database owner since the importance of each sensitive itemset varies. In this paper we propose a distortion-based itemset hiding algorithm that allows database owner to assign multiple sensitive thresholds, namely itemset oriented pseudo graph based sanitization (IPGBS) algorithm. The purpose of IPGBS algorithm is to give minimum distortion to the nonsensitive knowledge and data while hiding all sensitive itemsets. For this reason, the IPGBS algorithm modifies least amount of transaction and transaction content. The performance evaluation of the IPGBS algorithm is conducted by using two different counterparts on four different databases. The results show that the IPGBS algorithm is more efficient in terms of nonsensitive frequent itemset loss on both dense and sparse databases. It has considerable good results in terms of number of transactions modified, number of items deleted, execution time and total memory allocation as well.Conference Object Citation - Scopus: 2Comparison of Dynamic Itemset Mining Algorithms for Multiple Support Thresholds(Association for Computing Machinery (ACM), 2017) Abuzayed, Nourhan; Ergenç, BelginMining1 frequent itemsets is an important part of association rule mining process. Handling dynamic aspect of databases and multiple support threshold requirements of items are two important challenges of frequent itemset mining algorithms. Most of the existing dynamic itemset mining algorithms are devised for single support threshold whereas multiple support threshold algorithms are static. This work focuses on dynamic update problem of frequent itemsets under multiple support thresholds and proposes tree-based Dynamic CFP-Growth++ algorithm. Proposed algorithm is compared to our previous dynamic algorithm Dynamic MIS [50] and a recent static algorithm CFP-Growth++ [2] and, findings are; in dynamic database, 1) both of the dynamic algorithms are better than the static algorithm CFP-Growth++, 2) as memory usage performance; Dynamic CFP-Growth++ performs better than Dynamic MIS, 3) as execution time performance; Dynamic MIS is better than Dynamic CFP-Growth++. In short, Dynamic CFP-Growth++ and Dynamic MIS have a trade-off relationship in terms of memory usage and execution time.