Master Degree / Yüksek Lisans Tezleri

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

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2008 - 20102

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Subject: search.filters.subject.Data minning

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  • Master Thesis
    Mining Xml Documents With Association Rule Algorithms
    (Izmir Institute of Technology, 2008) Gürel, Görkem; Püskülcü, Halis
    Following the increasing use of XML technology for data storage and data exchange between applications, the subject of mining XML documents has become more researchable and important topic. In this study, we considered the problem of Mining Association Rules between items in XML document. The principal purpose of this study is applying association rule algorithms directly to the XML documents with using XQuery which is a functional expression language that can be used to query or process XML data. We used three different algorithms; Apriori, AprioriTid and High Efficient AprioriTid. We give comparisons of mining times of these three apriori-like algorithms on XML documents using different support levels, different datasets and different dataset sizes.
  • Master Thesis
    Impacts of Frequent Itemset Hiding Algorithms on Privacy Preserving Data Mining
    (Izmir Institute of Technology, 2010) Yıldız, Barış; Ergenç, Belgin
    The 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.