Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

Browse

Filters

2018 - 201912020 - 20221

Settings

Scopus Q: search.filters.scopusQuartile.Q2Subject: search.filters.subject.Data mining

Search Results

Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 1
    Citation - Scopus: 3
    Ignoring Internal Utilities in High-Utility Itemset Mining
    (MDPI, 2022) Oğuz, Damla
    High-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: 9
    Citation - Scopus: 11
    A Qualitative Survey on Frequent Subgraph Mining
    (De Gruyter, 2018) Güvenoğlu, Büşra; Ergenç Bostanoğlu, Belgin
    Data mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large databases. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. So, graph mining is one of the most popular subdivisions of data mining. Subgraphs that are more frequently encountered than the user-defined threshold in a database are called frequent subgraphs. Frequent subgraphs in a database can give important information about this database. Using this information, data can be classified, clustered and indexed. The purpose of this survey is to examine frequent subgraph mining algorithms (i) in terms of frequent subgraph discovery process phases such as candidate generation and frequency calculation, (ii) categorize the algorithms according to their general attributes such as input type, dynamicity of graphs, result type, algorithmic approach they are based on, algorithmic design and graph representation as well as (iii) to discuss the performance of algorithms in comparison to each other and the challenges faced by the algorithms recently.