Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Ergenç, BelginSubject: search.filters.subject.Data mining

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Now showing 1 - 2 of 2
  • Master Thesis
    Comparison of Dynamic Rule Mining Algorithms
    (Izmir Institute of Technology, 2012) Shariff, Karunda; Ergenç, Belgin
    In real life, new data is constantly added to databases while the existing one is modified or deleted. The new challenge of association rule mining is the need to always maintain meaningful association rules whenever the databases are updated. Many dynamic algorithms that use different techniques have been proposed in the past to deal with this challenge. However less work has been done in comparing their performance. In this study comparison of two dynamic rule mining algorithms; Dynamic Matrix Apriori and Fast Update 2, which have not been compared in the past, is done. The algorithms are tested on three different datasets to determine their execution time with updates of: additions, deletions and different support thresholds. Our findings reveal that DMA performs better with two dataset and so is FUP2 with the other dataset. The difference in performance of the two algorithms is mainly caused by the nature of the datasets.
  • Master Thesis
    Dynamic Frequent Itemset Mining Based on Matrix Appriori Algorithm
    (Izmir Institute of Technology, 2012) Oğuz, Damla; Oğuz, Damla; Ergenç Bostanoğlu, Belgin; Ergenç, Belgin
    The 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.