Veri Madenciliğinde Kümeleme Teknikleri: Yöntemler, Zorluklar ve Uygulamalar Üzerine Bir Araştırma

Yıl 2024, , 32 - 50, 06.06.2024

Tasnim Alasalı Yasin Ortakcı

https://doi.org/10.53070/bbd.1421527

Öz

Kümeleme, veri madenciliğinin hem araştırma hem de pratik uygulamalarında önemli bir tekniktir. Geleneksel olarak, anlamlı içgörüler elde etmek için etiketsiz verilerin düzenlenmesini kolaylaştıran önemli bir analitik metot olarak işlev yapmaktadır. Kümeleme zorluklarının doğasında var olan karmaşıklık, farklı tipte kümeleme algoritmalarının geliştirilmesini sağlamıştır. Bu algoritmaların her biri, belirli veri kümeleme senaryolarını ele almak için uyarlanmıştır. Bu bağlamda, bu makale, çeşitli alanlardaki zorlukları ve uygulamaları da dahil olmak üzere veri madenciliğindeki kümeleme tekniklerinin kapsamlı bir analizini sunmaktadır. Ayrıca, mesafe tabanlı, hiyerarşik, grid tabanlı ve yoğunluk tabanlı algoritmaları kapsayan farklı kümeleme metodolojilerini karakterize eden güçlü yönler ve sınırlamalar hakkında kapsamlı bir araştırma yapmaktadır. Ek olarak, sağlık hizmetleri, görüntü işleme, metin ve belge kümeleme ve büyük veri analitiği alanı dahil ancak bunlarla sınırlı olmamak üzere çeşitli alanlarda çok sayıda kümeleme algoritması örneği ve bunların ampirik sonuçları açıklanmaktadır.

Anahtar Kelimeler

Kümeleme, hiyerarşik, mesafe-tabanlı, grid tabanlı, yoğunluk tabanlı, veri madenciliği

Clustering Techniques in Data Mining: A Survey of Methods, Challenges, and Applications

Öz

Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.

Anahtar Kelimeler

Clustering, hierarchical, distance-based, grid-based, density-based, data mining

Kaynakça

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