GCRIS

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Comparison of Document Classification Approaches for Turkish Texts
(Izmir Institute of Technology, 2015) Çobanoğlu, Özlem Ece; Aslan, Burak Galip
Internet usage is exponentially growing day by day. This rapid growth in Internet usage leads to an explosion in the number of electronic documents being produced daily. The huge bulk of documents make it difficult accessing the necessary and relevant information. Due to lack of logical organization, retrieval and processing of the desired information from huge number of documents becomes a complex and time consuming task with human effort. Therefore, document classification is significant task to manage and process the documents. In this thesis, the performance of different classification approaches produced from several algorithms is thoroughly evaluated. The main goal of the thesis is to determine the best combination of document preprocessing steps and classification algorithms. Different feature weighting, construction and selection methods are experimented on Turkish documents. Stemmed and original words and their bi-gram and tri-gram forms are used to construct the features which represent the documents. The effects of several weighting algorithms and the combination of feature selection and weighting algorithms on 3 different classification approaches are interpreted. The performance of 216 different classification process combinations are analyzed. Experimental results show that C4.5 (C4.5 Decision Tree) classification algorithm has the highest accuracy results in 95% of the results. SVM (Support Vector Machine) algorithm produces the closest results to C4.5 and it provides the highest accuracy in 5% of the experimental results. NB (Naive Bayes) algorithm has always the lowest accuracy rate in these 3 different classification algorithm results.
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.
Heuristic Container Placement Algorithms
(Izmir Institute of Technology, 2003) Aslan, Burak Galip; Püskülcü, Halis
With the growth of transportation over sea; defining transportation processes in a better way and finding ways to make transportation processes more effective have become one of the most important research areas of today. Especially in the last quartet of the previous decade, the computers had become much powerful tools with their impressive amount of data processing cababilites. It was imminent that computers had begun taking serious roles in the system development studies. As a result; constructing models for the processes in container terminals and processing the data with the computers create opportunities for the automation of various processes in container terminals. The final step of these studies is the full automation of terminal activities with software packages that combine various functions focused on various processes in a single system.This study is about a project that had been made for a container terminal owned by a special company. During this study; there had been discussions with experts about the subject, and container handling processes in the terminal had been analyzed in order to define the main structure of the yard management software to be created.This study focuses on the container handling activities over the yard space so as to create a basis for a computer system that will take part in the decisions during the container operations. Object oriented analysis and design methods are used for the definition of the system that will help the decisions in the yard operations. The optimization methodology that will be the core of the container placement decisions is based on using different placement patterns and placement algorithms for different conditions. These placement patterns and algorithms are constructed due to the container handling machinery that was being used in the terminal that this study has been made for.
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 Degree / Yüksek Lisans Tezleri

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