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A useful reference for professionals and students in engineering and social sciences.
Item #:
171543623
Veri Madenciliği Yöntemleri: Bilgisayar Bilimleri ve Bilgisayar Mühendisliği
Item #:
171543623
R$ 149
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- **Satın alacağınız ürünün kapak görseli katalogtaki ile farklılık gösterebilir.Bu kitap, veri madenciliği yöntemlerini ele alan temel ve nitelikli bir eserdir. Sınıflama, kümeleme ve birliktelik kurallarıyla ilgili birçok algoritma en yalın biçimiyle incelenmiştir.Veri madenciliği, kurumların zaman içerisinde sahip olduğu büyük boyutlardaki verilerden alışılagelen istatistik yöntemlerle elde edilemeyen veya elde edilmesi güç olan bilgileri elde etmek için bir çeşit verileri işleme ve çözümleme yöntemidir. Yani sahip olduğumuz veri kümesinden işimize yarayacak yararlı bilgiyi üretme yöntemidir.Veri Madenciliği adlı bu kitabımızda sırasıyla veri ambarı kavramı, veri madenciliği kavramı ve kullanım alanları, veri madenciliğinde kullanılan sınıflandırma algoritmaları, Twoing ve Gini algoritmaları, en yakın k-komşu algoritması, kümeleme konusu, hiyerarşik ve hiyerarşik olmayan kümeleme biçimi, sepet çözümlemeleri adıyla bilinen birliktelik kuralları, Bayes ağları ve Destek Vektör Makineleri konusu ele alınmıştır; herbiri bir bölüm olarak düzenlenmiştir.Böylesi bir Veri Madenciliği kitabı hem profesyoneller için yararlı bir başvuru kitabı hem de bilgisayar mühendisliği, matematik-bilgisayar, yönetim bilişim sistemleri, endüstri mühendisliği, yazılım mühendisliği gibi mühendislik ve sosyal bilimlerde dört yıllık lisans ve meslek yüksek okulu öğrencileri için yararlı bir ders kitabı özelliğindedir. Daha fazla göster
| Yayıncı | Papatya Yayıncılık |
| Yayınlanma Tarihi | 25 Ağustos 2016 |
| Baskı | 3. |
| Dil | Türkçe |
| Baskı Uzunluğu | 240 sayfa |
| ISBN-10 | 9756797827 |
| ISBN-13 | 978-9756797822 |
| Item Weight | 1.5 lbs (680 grams) |
Product Description
Veri Madenciliği Yöntemleri: Bilgisayar Bilimleri ve Bilgisayar Mühendisliği
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R$ 149
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Features & Benefits
- Comprehensive guide to data mining methods and algorithms.
- Explains classification, clustering, and association rules clearly.
- Covers essential concepts like data warehouse and data mining applications.
- Includes practical algorithms: Twoing, Gini, k-nearest neighbor.
- Suitable for both professionals and undergraduate students.
- Ideal for disciplines in computer science and engineering.
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