Ergenç Bostanoğlu, Belgin
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Ergenc, Belgin
Ergenç, Beigin
Bostanoğlu, Belgin Ergenç
Bostanoglu, Belgin Ergenc
Ergenç, Belgin
Ergenc Bostanoglu, Belgin
Özakar, Belgin
Ergenç, Beigin
Bostanoğlu, Belgin Ergenç
Bostanoglu, Belgin Ergenc
Ergenç, Belgin
Ergenc Bostanoglu, Belgin
Özakar, Belgin
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Email Address
belginergenc@iyte.edu.tr
Main Affiliation
03.04. Department of Computer Engineering
Status
Current Staff
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1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
6
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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Documents
24
Citations
130
h-index
6
Documents
16
Citations
61
Scholarly Output
43
Articles
15
Views / Downloads
62238/15899
Supervised MSc Theses
12
Supervised PhD Theses
4
WoS Citation Count
65
Scopus Citation Count
161
Patents
0
Projects
1
WoS Citations per Publication
1.51
Scopus Citations per Publication
3.74
Open Access Source
38
Supervised Theses
16
| Journal | Count |
|---|---|
| PeerJ Computer Science | 4 |
| International Journal of Data Warehousing and Mining | 3 |
| Symmetry | 2 |
| 21st International Database Engineering and Applications Symposium, IDEAS 2017 | 1 |
| Computer Systems Science and Engineering | 1 |
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43 results
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Now showing 1 - 10 of 43
Conference Object Citation - WoS: 2Citation - Scopus: 2Dynamic Itemset Mining Under Multiple Support Thresholds(IOS Press, 2016) Abuzayed, Nourhan; Ergenç Bostanoğlu, Belgin; Ergenç, BelginHandling dynamic aspect of databases and multiple support threshold requirements of items are two important challenges of frequent itemset mining algorithms. Existing dynamic itemset mining algorithms are devised for single support threshold whereas multiple support threshold algorithms assume that the databases are static. This paper focuses on dynamic update problem of frequent itemsets under MIS (Multiple Item Support) thresholds and introduces Dynamic MIS algorithm. It is i) tree based and scans the database once, ii) considers multiple support thresholds, and iii) handles increments of additions, additions with new items and deletions. Proposed algorithm is compared to CFP-Growth++ and findings are; in dynamic database 1) Dynamic MIS performs better than CFP-Growth++ since it runs only on increments and 2) Dynamic MIS can achieve speed-up up to 56 times against CFP-Growth++.Master Thesis Dynamic Frequent Itemset Mining Based on Matrix Appriori Algorithm(Izmir Institute of Technology, 2012) Oğuz, Damla; Oğuz, Damla; Ergenç Bostanoğlu, Belgin; Ergenç, BelginThe frequent itemset mining algorithms discover the frequent itemsets from a database. When the database is updated, the frequent itemsets should be updated as well. However, running the frequent itemset mining algorithms with every update is inefficent. This is called the dynamic update problem of frequent itemsets and the solution is to devise an algorithm that can dynamically mine the frequent itemsets. In this study, a dynamic frequent itemset mining algorithm, which is called Dynamic Matrix Apriori, is proposed and explained. In addition, the proposed algorithm is compared using two datasets with the base algorithm Matrix Apriori which should be re-run when the database is updated.Conference Object Citation - WoS: 7Citation - Scopus: 20Vertical Pattern Mining Algorithm for Multiple Support Thresholds(Elsevier Ltd., 2017) Darrab, Sadeq; Ergenç Bostanoğlu, Belgin; Ergenç, BelginFrequent pattern mining is an important task in discovering hidden items that co-occur (itemset) more than a predefined threshold in a database. Mining frequent itemsets has drawn attention although rarely occurring ones might have more interesting insights. In existing studies, to find these interesting patterns (rare itemsets), user defined single threshold should be set low enough but this results in generation of huge amount of redundant itemsets. We present Multiple Item Support-eclat; MIS-eclat algorithm, to mine frequent patterns including rare itemsets under multiple support thresholds (MIS) by utilizing a vertical representation of data. We compare MIS-eclat to our previous tree based algorithm, MISFP-growth28 and another recent algorithm, CFP-growth++22 in terms of execution time, memory usage and scalability on both sparse and dense databases. Experimental results reveal that MIS-eclat and MISFP-growth outperform CFP-growth++ in terms of execution time, memory usage and scalability.Conference Object Citation - Scopus: 4Itemset Hiding Under Multiple Sensitive Support Thresholds(SCITEPRESS, 2017) Öztürk, Ahmet Cumhur; Ergenç Bostanoğlu, BelginItemset mining is the challenging step of association rule mining that aims to extract patterns among items from transactional databases. In the case of applying itemset mining on the shared data of organizations, each party needs to hide its sensitive knowledge before extracting global knowledge for mutual benefit. Ensuring the privacy of the sensitive itemsets is not the only challenge in the itemset hiding process, also the distortion given to the non-sensitive knowledge and data should be kept at minimum. Most of the previous works related to itemset hiding allow database owner to assign unique sensitive threshold for each sensitive itemset however itemsets may have different count and utility. In this paper we propose a new heuristic based hiding algorithm which 1) allows database owner to assign multiple sensitive threshold values for sensitive itemsets, 2) hides all user defined sensitive itemsets, 3) uses heuristics that minimizes loss of information and distortion on the shared database. In order to speed up hiding steps we represent the database as Pseudo Graph and perform scan operations on this data structure rather than the actual database. Performance evaluation of our algorithm Pseudo Graph Based Sanitization (PGBS) is conducted on 4 real databases. Distortion given to the nonsensitive itemsets (information loss), distortion given to the shared data (distance) and execution time in comparison to three similar algorithms is measured. Experimental results show that PGBS is competitive in terms of execution time and distortion and achieves reasonable performance in terms of information loss amongst the other algorithms. © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.Doctoral Thesis Graphlet mining in big data(01. Izmir Institute of Technology, 2024) Ergenç Bostanoğlu, Belgin; Çalmaz, Büşra; Bostanoğlu, Belgin ErgençThis thesis explores graphlet counting algorithms, which are crucial for understanding the structural principles of complex networks such as bioinformatics, social networks, and network model evaluation. Counting graphlets in large networks is computationally challenging due to the combinatorial explosion of possibilities, particularly for larger graphlet sizes. To address this, we focus on clique graphlets, fully connected subgraphs, which reveal critical patterns in areas like protein structure analysis, social network modeling, community detection, and spam detection. Counting k-cliques (subgraphs with $k$ nodes) becomes infeasible for large datasets and high $k$ values. Existing exact and approximate algorithms struggle with large $k$, often failing when $k$ exceeds 10. To tackle these limitations, we propose BDAC (Boundary-Driven Approximations of K-Cliques), a novel algorithm that efficiently approximates k-clique counts using classical extremal graph theorems. BDAC uniquely provides lower and upper bounds for k-clique counts at both local (per vertex) and global levels, making it particularly suited for large, dense graphs with high $k$ values. Unlike existing methods, the algorithm's complexity remains unaffected by the value of $k$. We validate BDAC's efficiency and scalability through extensive comparisons with leading algorithms on diverse datasets, spanning k values from minor (e.g., 8) to large (e.g., 50). Parallelization techniques enhance its performance, making it highly scalable for analyzing large and dense networks. BDAC offers a significant advancement in k-clique counting, enabling the analysis of previously considered computationally intractable networks.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: 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.Master Thesis Impacts of Frequent Itemset Hiding Algorithms on Privacy Preserving Data Mining(Izmir Institute of Technology, 2010) Yıldız, Barış; Ergenç, BelginThe invincible growing of computer capabilities and collection of large amounts of data in recent years, make data mining a popular analysis tool. Association rules (frequent itemsets), classification and clustering are main methods used in data mining research. The first part of this thesis is implementation and comparison of two frequent itemset mining algorithms that work without candidate itemset generation: Matrix Apriori and FP-Growth. Comparison of these algorithms revealed that Matrix Apriori has higher performance with its faster data structure. One of the great challenges of data mining is finding hidden patterns without violating data owners. privacy. Privacy preserving data mining came into prominence as a solution. In the second study of the thesis, Matrix Apriori algorithm is modified and a frequent itemset hiding framework is developed. Four frequent itemset hiding algorithms are proposed such that: i) all versions work without pre-mining so privacy breech caused by the knowledge obtained by finding frequent itemsets is prevented in advance, ii) efficiency is increased since no pre-mining is required, iii) supports are found during hiding process and at the end sanitized dataset and frequent itemsets of this dataset are given as outputs so no post-mining is required, iv) the heuristics use pattern lengths rather than transaction lengths eliminating the possibility of distorting more valuable data.Article Citation - WoS: 3Citation - Scopus: 4Full-Exact Approach for Frequent Itemset Hiding(IGI Global Publishing, 2015) Ayav, Tolga; Ergenç, BelginThis paper proposes a novel, exact approach that relies on integer programming for association rule hiding. A large panorama of solutions exists for the complex problem of itemset hiding: from practical heuristic approaches to more accurate exact approaches. Exact approaches provide better solutions while suffering from the lack of performance and existing exact approaches still augment their methods with heuristics to make the problem solvable. In this case, the solution may not be optimum. This work present a full-exact method, without any need for heuristics. Extensive tests are conducted on 10 real datasets to analyze distance and information loss performances of the algorithm in comparison to a former similar algorithm. Since the approach provides the optimum solution to the problem, it should be considered as a reference method.Article k-Clique counting on large scale-graphs: a survey(Peerj inc, 2024) Calmaz, Busra; Ergenç Bostanoğlu, Belgin; Bostanoglu, Belgin ErgencClique counting is a crucial task in graph mining, as the count of cliques provides different insights across various domains, social and biological network analysis, community detection, recommendation systems, and fraud detection. Counting cliques is algorithmically challenging due to combinatorial explosion, especially for large datasets and larger clique sizes. There are comprehensive surveys and reviews on algorithms for counting subgraphs and triangles (three-clique), but there is a notable lack of reviews addressing k-clique counting algorithms for k > 3. This paper addresses this gap by reviewing clique counting algorithms designed to overcome this challenge. Also, a systematic analysis and comparison of exact and approximation techniques are provided by highlighting their advantages, disadvantages, and suitability for different contexts. It also presents a taxonomy of clique counting methodologies, covering approximate and exact methods and parallelization strategies. The paper aims to enhance understanding of this specific domain and guide future research of k-clique counting in large-scale graphs.