Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Citation - Scopus: 3Integrated Approach for Privacy Preserving Itemset Mining(Springer, 2012) Yıldız, Barış; Ergenç, BelginIn this work, we propose an integrated itemset hiding algorithm that eliminates the need of pre-mining and post-mining and uses a simple heuristic in selecting the itemset and the item in itemset for distortion. Base algorithm (matrix-apriori) works without candidate generation so efficiency is increased. Performance evaluation demonstrates (1) the side effect (lost itemsets) and time while increasing the number of sensitive itemsets and support of itemset and (2) speed up by integrating the post mining. © 2012 Springer Science+Business Media, LLC.Conference Object Citation - Scopus: 2Hiding Sensitive Predictive Frequent Itemsets(International Association of Engineers, 2011) Yıldız, Barış; Ergenç, BelginIn this work, we propose an itemset hiding algorithm with four versions that use different heuristics in selecting the item in itemset and the transaction for distortion. The main strengths of itemset hiding algorithm can be stated as i) it works without pre-mining so privacy breech caused by revealing frequent itemsets in advance is prevented and efficiency is increased, ii) base algorithm (Matrix-Apriori) works without candidate generation so efficiency is increased, iii) sanitized database and frequent itemsets of this database are given as outputs so no post-mining is required and iv) simple heuristics like the length of the pattern and the frequency of the item in the pattern are used for selecting the item for distortion. We compare versions of our itemset hiding algorithm by their side effects, runtimes and distortion on original database.Conference Object Citation - Scopus: 16Comparison of Two Association Rule Mining Algorithms Without Candidate Generation(ACTA Press, 2010) Yıldız, Barış; Ergenç, BelginAssociation rule mining techniques play an important role in data mining research where the aim is to find interesting correlations among sets of items in databases. Although the Apriori algorithm of association rule mining is the one that boosted data mining research, it has a bottleneck in its candidate generation phase that requires multiple passes over the source data. FP-Growth and Matrix Apriori are two algorithms that overcome that bottleneck by keeping the frequent itemsets in compact data structures, eliminating the need of candidate generation. To our knowledge, there is no work to compare those two similar algorithms focusing on their performances in different phases of execution. In this study, we compare Matrix Apriori and FP-Growth algorithms. Two case studies analyzing the algorithms are carried out phase by phase using two synthetic datasets generated in order i) to see their performance with datasets having different characteristics, ii) to understand the causes of performance differences in different phases. Our findings are i) performances of algorithms are related to the characteristics of the given dataset and threshold value, ii) Matrix Apriori outperforms FP-Growth in total performance for threshold values below 10%, iii) although building matrix data structure has higher cost, finding itemsets is faster.