Novel Approach to Minimize the Memory Requirements of Frequent Subgraph Mining Techniques

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dc.authorid0000-0002-9245-5728en_US
dc.authorscopusid22433933900en_US
dc.contributor.authorBilgin, Turgay Tugay
dc.contributor.authorMurat, Oğuz
dc.date.accessioned2022-04-21T06:04:44Z
dc.date.available2022-04-21T06:04:44Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractFrequent subgraph mining (FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing (PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, gSpan and Gaston. We compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38% depending on the dataset.en_US
dc.identifier.doi10.1049/cje.2021.01.003en_US
dc.identifier.endpage267en_US
dc.identifier.issn10224653
dc.identifier.issue2en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage258en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1944
dc.identifier.volume30en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBilgin, Turgay Tugay
dc.language.isoenen_US
dc.publisherJohn Wiley and Sons Incen_US
dc.relation.ispartofChinese Journal of Electronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData Miningen_US
dc.subjectFrequent Subgraphsen_US
dc.subjectMemoryen_US
dc.subjectSpace Complexityen_US
dc.titleNovel Approach to Minimize the Memory Requirements of Frequent Subgraph Mining Techniquesen_US
dc.typeArticleen_US

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