Classification and clustering
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Data analysis is the process of understanding or extracting patterns from raw data. One of the widely used methods in data analysis is machine learning. Machine learning is a system or model that can learn from raw data to make decisions without human intervention. Classification and clustering are the most popular machine learning technologies for the analysis of data. Each technology involves many algorithms that aim to categorize objects into classes depending on the object's features. In this chapter, we introduce a guide to both classification and clustering technology by applying different algorithms to different datasets. The classification dataset differs from the clustering dataset. The reason here is to explain the suitable type of data for each technology. For clustering, the k-means clustering algorithm is applied. For classification, the decision tree algorithm is applied. The results showed the efficiency of different machine learning algorithms for data analysis and decision-making. © 2024 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
Kaynak
Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Tareq, W. Z. T., & Davud, M. (2024). Classification and clustering. In Decision-Making Models (pp. 351-359). Academic Press.
