Veri Tabanlarında Bilgi Keşfine Formel Bir Yaklaşım:Kısım I: Eşleştirme Sorguları ve Algoritmalar

Sever, Hayri and Oğuz, Buket Veri Tabanlarında Bilgi Keşfine Formel Bir Yaklaşım:Kısım I: Eşleştirme Sorguları ve Algoritmalar. Bilgi Dünyası, 2002, vol. 3, n. 2, pp. 173-204. [Journal article (Paginated)]

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English abstract

In the last two decades, we have witnessed an explosive growth in our capabilities to both collect and store data, and generate even more data by further computer processing. In fact, it is estimated that the amount of information in the world doubles every 20 months. Our inability to interpret and digest these data, as readily as they are accumulated, has created a need for a new generation of tools and techniques for automated and intelligent database analysis. Consequently, the discipline of knowledge discovery in databases (KDD), which deals with the study of such tools and techniques, has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) valuable, interesting and previously unknown knowledge from very large real-world databases. Many aspects of KDD have been investigated in several related fields such as database systems, machine learning, intelligent information systems, statistics, and expert systems. In the first part of our study (Part I), we discuss the fundamental issues of KDD as well as its process oriented view with a special emphasis on modelling association rules. In the second part (Part II), a follow-up study of this article, association queries will be modelled by formal concept analysis.

Turkish abstract

Son yirmi yıldır veri toplama ve saklama kapasitesinde çok ani büyümeye şahit olmaktayız. Öyleki, bir bilgisayarın işleyebileceği veriden daha fazlası üretilmektedir. Gerçekte bu durum, dünyadaki bilgi miktarının her 20 ayda bir ikiye katlandığı varsayımı ile uygunluk arz etmektedir. Veri biriktirilmesi ile eş zamanlı olarak onu yorumlamadaki ve özümsemedeki insanoğlunun yetersizliği, özdevimli ve akıllı veri tabanı analizi için, yeni nesil araçlarına ve tekniklerine olan ihtiyacı doğurdu. Sonuç olarak, büyük hacimli veri tabanlarından değerli, ilginç ve önceden bilinmeyen bilgiyi keşfetme (veya çıkarma) problemi ile eşleştirilen pratik uygulamalar ve olası çözümlerin kuramsal zorlukları nedeni ile, veri tabanlarında bilgi keşfi (VTBK) önemli ve aktif bir araştırma alanına evrimleşti. Veri tabanı sistemleri, makine öğrenimi, akıllı bilgi sistemleri, istatistik ve uzman sistemler gibi birbirleri ile yakından ilişkili alanlarca VTBK’nın birçok yönü incelendi. Çalışmamızın ilk kısmında (Kısım I), VTBK’ya süreç esaslı bakış açısı getireceğiz ve onun temel sorunlarını adresleyeceğiz. Açık olarak, VTBK disiplinine taban oluşturan gerçek-hayat verilerinin karakteristik özellikleri verilecek ve takiben veri madenciliği ve özelinde eşleştirme sorguları işlenecektir. Eşleştirme sorgularına getirilen tipik bir çözüm açıklanacak ve etkinlik ölçütleri değerlendirilecektir. Bu makalenin devamı olarak yayınlanacak olan ikinci kısımda ise (Kısım II), biçimsel kavram analizi aracılığı ile eşleştirme kuralları modellenmesine özgün yaklaşımımız sunulacaktır.

Item type: Journal article (Paginated)
Keywords: Biçimsel kavram analizi, eşleştirme sorguları, bağımlılık ilişkileri, kavram yapıları, formal concept analysis, association query, dependency relationships, concept structures
Subjects: H. Information sources, supports, channels. > HL. Databases and database Networking.
L. Information technology and library technology
L. Information technology and library technology > LN. Data base management systems.
Depositing user: Kamil Comlekci
Date deposited: 26 Mar 2006
Last modified: 02 Oct 2014 12:02
URI: http://hdl.handle.net/10760/7348

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