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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. 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Hastane Bilgi Yönetim Sistemleri kamu ve özel hastanelerinin tamamımda kullanılmaktadır. Yirmi dört saat kesintisiz olarak yeni tıbbi verilerin kaydedildiği bu geniş veri tabanlarında, biriken bu değerli veriler günümüzde sadece kurum içinde gerekli raporlama ve sorgular için kullanılmakta ve temiz veri ambarları şeklinde tutulamadığından her hangi bir akademik çalışma için değerlendirilemediğine tanık olmaktayız. Bu tezde tıbbi verilerin belirli sınırlar dâhilinde ve standart protokoller ile akademik çalışmalar için açık kaynak olması gerektiğine vurgu yapılacak, gerçekleştirdiğim tıbbi bilişim sistemleri ve tıbbi uygulamalarda bu verilerin işlenip nasıl kıymetli sonuçların elde edilebileceğinden bahsedilecektir. Tezdeki çalışmalar `tıbbi laboratuarlar`, `yüz ve boyun kanserleri` ve `beslenme ve diyet` alanları üzerine yapılmıştır. Bu üç alan için MSSQL veri tabanı ve C# programlama dili ile ASP.NET kullanılarak yeni bilişim sistemleri geliştirilmiştir. Böylece, temiz ve işlenebilir verilerin bu sistemlere toplanması sağlanmış ve veri madenciliği ve bilgi keşfi tekniği bu bilişim sistemleri için farklı algoritma yaklaşımları ile uygulanabilmiştir.Çalışmalarda veri madenciliği tekniklerinden farklı yaklaşımlara sahip kümeleme algoritmaları geliştirilmiştir. İlk olarak, bu algoritmalar etik kurul izinleri alınmış laboratuar verilerinin analizi için kullanılmıştır. İkinci olarak, yine etik kurul izinleri alınmış yüz ve boyun kanser verilerinin analizi için farklı yaklaşımlı kümeleme algoritmalar kullanılmıştır. Son olarak beslenme ve diyet veri kümesi melez kuantum genetik algoritması ile optimize edilmiştir.Geliştirilen kümeleme algoritmaları kendi kendini düzenleyen haritalar, k-ortalama++ algoritmaları ve onların farklı yaklaşımları ile karşılaştırılmıştır. Bunun yanında, geliştirilmiş melez kuantum genetik algoritması, geleneksel genetik algoritma ve kuantum genetik algoritma ile karşılaştırılmıştır. Sonuç olarak da geleneksel yöntemlere nazaran daha hızlı ve doğruluğu daha yüksek desenler elde edilebilmiştir. Hospital Information Management Systems are used in all public and private hospitals. Valuable data which is obtained from these big databases, where updated data are collected continuously during all day, is used for only some necessary reports and queries inside of the corporation, and this data may not be considered for an academic study, because it is not held as a clean data warehouse. In this thesis, it is underlined that this data should be an open source with standard protocols under definite boundaries for academic studies in order to supply the improvement of medical research, and it is mentioned that how this data is processed and valuable patterns are obtained. There are studies about three branches, `medical laboratories`, `head and neck cancers` and `nutrition and diets` in this thesis. For these three branches, new information systems have been implemented by using the technologies of MSSQL database and ASP.NET with C# programming languages. Thus, it has supplied to collect clean and processable instances in them and, the technique of knowledge discovery and data mining was able to be applied for these information systems by using different algorithm approaches.In the studies, clustering algorithms from data mining techniques have been implemented with different approaches and firstly, these algorithms have been used to analysis laboratory data sets which necessary permissions have been obtained to use. Secondly, head and neck cancer instances, which necessary permissions have been obtained to use, too, have been analysed by using clustering algorithms with different approaches. Lastly, the data set of nutrition and diets is optimized by hybrid quantum genetic algorithm.Improved clustering algorithms have been compared with self-organizing map, k-means++ algorithms and its different approaches. Moreover, improved hybrid quantum genetic algorithm has been compared with traditional genetic algorithm and quantum genetic algorithm. As a result, the patterns, which have higher accuracy and performance than the traditional approaches, have been obtained. 151

Kablosuz ağ servisi yapan bir sunucu güçlü güvenlik sistemlerine ihtiyaç duymaktadır. Bu amaç için, ağ güvenliğine, aykırı durum tespiti, kümeleme ve sınıflandırma gibi veri madenciliği paradigmaları kullanılarak yeni bir perspektif kazandırılır. Bu çalışma hem ilk aşamadaki kümeleme hem de sonrasındaki sınıflandırma için en yakın k komşu algoritmasını kullanır. En yakın k komşu algoritması kümeleme için kullanıcı profillerini anlamlaştıran veri ambarına ihtiyaç duyar. Bu nedenle, sunucudan istekte bulunulduğu zaman aralıkları ve doküman madenciliğinden geçmiş, istek IP adresleri kullanılacaktır. Ağdaki kullanıcılar, yeni bir yaklaşımla, en yakın k komşu algoritmasının k ve eşik değer parametrelerinin uygun değerlerinin hesaplanması ile kümelenir. Sonuç olarak, oluşan bu kümeler üzerinden, öncelikli ağırlık değerleri farklı olan, farklı eşik değerlerle ve öncelikli ağırlık değerleri farklı olan ortalama benzerliklerle, yeni isteklerin bir aykırı durummu yoksa normal mi oldukları ayırt edilebilecektir. A server which serves wireless network needs strong security systems. For this aim, a new perspective to network security is won by using data mining paradigms like outlier detection, clustering and classification. This study uses k-Nearest Neighbor algorithm for both firstly clustering and then classification. K- NearestNeighbor algorithm needs data warehouse which impersonates user profiles to cluster. Therefore, requested time intervals and requested IPs with text mining are used for user profiles. Users in the network are clustered by calculating optimum k and threshold parameters of k-Nearest Neighbor algorithm with a new approach. Finally, over these clusters, new requests are separated as outlier or normal bydifferent threshold values with different priority weight values and average similarities with different priority weight values. 80