Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Abstract

Günümüz teknolojisinin gelişmesine paralel olarak bilgisayar ortamına aktarılmış olan veri miktarı inanılmaz boyutlara ulaşmış ve gün geçtikçe de artmaktadır.  Bu nedenle veriyi işleme yöntemleri de değişmektedir. Klasik kümeleme yaklaşımlarında veri statiktir. Oysa günümüz teknolojisinde, verinin çok hızlı olduğu dünyada artık veriyi akarken kümeleyecek, kullanıcıya istediği zaman sonuç verebilecek uygulamalara ihtiyaç vardır. Bu anlamda ihtiyacı karşılayan akan veri kümeleme yaklaşımlarına olan talep gün geçtikçe artmaktadır. Çünkü akan veri kümeleme yaklaşımları bir defa okumalı, hızlı ve kendisini yeni gelen veriye uyarlama özelliğine sahiptir. Yani veri bir yandan akarken bir yandan kullanıcıya sonuç üretilebilmektedir. Bu çalışmada akan veri kümeleme alanında yapılan çalışmalar derlenmekte ve bu alana ilgi duyan araştırmacılara ışık tutulmaktır.

Keywords

• Akan veri kümeleme,akan veri kümeleme teknikleri,akan veri kümeleme alanındaki ihtiyaçlar

A Survey on Data Stream Clustering Techniques

Abstract

In parallel with the development of today's technology, the amount of data that has been transferred to the computer environment has reached incredible dimensions and is increasing day by day. For this reason, the methods of data processing are also changing. In classical data clustering approaches, data is static. However, in today's technology in which data streams very fast, there is a need for applications that can cluster data and show results while the data is streaming whenever the user wants. In this sense, the demand for data stream clustering approaches is increasing day by day. Because, the data stream clustering approaches read once, fast, and have the ability to adapt themselves to new data. In other words, the results are shown to the user on the one hand, while the data is streaming on the other hand. In this study, the proposed studies on the data stream clustering area are collected and the researchers who are interested in this field are enlighten. 

Keywords

• Data stream clustering,data stream clustering techniques,demands in data stream clustering area

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