METE ÇELİK, AHMET ŞAKİR DOKUZ, FİLİZ DADAŞER ÇELİK

Hidroklimatik Veritabanlarında Oluşan ve Kaybolan Birliktelik Örüntülerinin Madenciliği

Oluşan ve kaybolan birliktelik örüntüleri, frekansları destekleri zamanla artan ve azalan birliktelik örüntüleri olarak tanımlanır. Bu örüntülerin keşfedilmesi finans ve haberleşme servisleri, halk sağlığı, ve hidroklimatik çalışmalar gibi uygulama alanları için önemlidir. Klasik birliktelik örüntü madenciliği algoritmaları birliktelik örüntülerinin zamanla nasıl değiştiklerini göz önüne almazlar. Bir birliktelik örüntüsünün destek değeri zamanla değişiyor ise bu örüntü oluşan veya kaybolan örüntü olarak tanımlanabilir. Bu çalışmada, zamanla gelişen birliktelik örüntülerinin oluşan ve kaybolan biliktelik örüntüleri gibi veri kümelerinden keşfine odaklanılmıştır. Bu örüntüleri keşfetmek için, Oluşan ve Kaybolan Birliktelik Örüntü Madencisi EVAPMiner olarak isimlendirilen özgün bir algoritma önerilmiştir. Önerilen algoritma, Türkiye hidroklimatik verileri üzerinde uygulanmıştır. Analizler, önerilen algoritmanın hidroklimatik veri kümelerindeki oluşan ve kaybolan birliktelik örüntülerinin keşfinde başarılı olduğunu ortaya koymuştur

Anahtar Kelimeler:

Birliktelik örüntü madenciliği, Veri madenciliği, Oluşan örüntüler, Hidroklimatik veri, Kaybolan örüntüler

Emerging and Vanishing Association Pattern Mining in Hydroclimatic Datasets

Emerging and vanishing association patterns can be defined as association patterns whose frequencies supports get stronger and weaker over time, respectively. Discovering these patterns is important for several application domains such as financial and communication services, public health, and hydroclimatic studies. Classical association pattern mining algorithms do not consider how the strengths of association patterns change over time. An association pattern can be defined as an emerging or vanishing pattern when its support measure changes over time. In this paper, we focus on discovery of time evolving association patterns i.e., emerging and vanishing association patterns from datasets. To discover such patterns, a novel algorithm, named as Emerging and Vanishing Association Pattern Miner EVAPMiner algorithm, was proposed. The proposed algorithm was evaluated using hydroclimatic dataset of Turkey. The analyses showed that the proposed algorithm successfully detects emerging and vanishing association patterns in hydroclimatic datasets.

Keywords:

Data Mining, Association Pattern Mining, Emerging Patterns, Vanishing Patterns, Hydroclimatic Data,

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