Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Year 2018, Issue: 13, 17 - 30, 31.08.2018

Ali Şenol , Hacer Karacan

https://doi.org/10.31590/ejosat.446019

Cited By: 5

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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