Mersin Üniversitesi
Ders Bilgileri
| DATA MINING | ||||||
|---|---|---|---|---|---|---|
| Kodu | Dönemi | Teori | Uygulama | Ulusal Kredisi | AKTS Kredisi | |
| Saat / Hafta | ||||||
| İBY310 | Spring | 3 | 0 | 3 | ||
| Ön Koşulu Olan Ders( ler ) | |
| Dili | en |
| Türü | Required |
| Seviyesi | Bachelor's |
| Öğretim Elemanı( ları ) | Okt.Jale BEKTAŞ |
| Öğretim Sistemi | Face to Face |
| Önerilen Hususlar | Yok |
| Staj Durumu | None |
| Amacı | The aim of this course, to get applicable informations that are not known before from wide databases and supply these informations proper to the usage goal with different variations of analyse methods. |
| İçeriği | Description of data mining. Overview to application areas, techniques and methods of discipline. data mining steps: Define the aim, create data cluster proper to aim(data selection), data cleaning and preprocessing, data compression and data transformation, choose data Mining algorithm, method assessment and representation, interpretation of information. Review data mining algorithms: association rules, desicion trees, classification, regression, relationship building, memory based methods, k-neighbour algorithm, bayesian classification, clustering methods. |
Dersin Öğrenim Çıktıları
| # | Öğrenim Çıktıları |
|---|---|
| 1 | Describe the definition and aim of data mining. |
| 2 | Identify overview to application areas, techniques and methods of discipline. |
| 3 | Identify data preprocessing steps for data warehouse concept. |
| 4 | Plan proper sollution to the problems and define data mining algorithms. |
| 5 | Apply association rules. |
| 6 | Analyse desicion trees and classification techniques. |
| 7 | Distinguish clustering methods. |
| 8 | Plan a sample warehouse with a real world data and analyse this data with all data mining algorithms. |
Haftalık Ayrıntılı Ders İçeriği
| # | Konular | Öğretim Yöntem ve Teknikleri |
|---|---|---|
| 1 | Description of data mining | Lectures, discussion |
| 2 | Overview to application areas, techniques and methods of discipline. data mining steps, OLAP | Lectures, discussion |
| 3 | Data preperation process and techniques for data mining | Lectures, discussion |
| 4 | Data preperation process and techniques for data mining | Lectures, discussion |
| 5 | Data preperation process and techniques for data mining | Lectures, discussion |
| 6 | Mining algorithm, method, association rules(Appriori algorithm) | Lectures, discussion |
| 7 | Mining algorithm, method, association rules(FP Growth algorithm) | Lectures, discussion |
| 8 | Mid-term examination | |
| 9 | Mining algorithm, method, classification and prediction | Lectures, discussion |
| 10 | Mining algorithm, method, classification and prediction(Bayesian classification) | Lectures, discussion |
| 11 | Application examples | Application |
| 12 | Data mining cluster analysis and data types | Application |
| 13 | Data mining clustering methods (k-neighbor algorithm) | Application |
| 14 | Data mining clustering methods(hierarchical methods, density based methods) | Application |
| 15 | Homework assessment | Application |
| 16 | Final Exam |
Resources
| # | Malzeme / Kaynak Adı | Kaynak Hakkında Bilgi | Referans / Önerilen Kaynak |
|---|---|---|---|
| 1 | Veri Madenciliği Mersin Üniversitesi, Ders Notu | ||
| 2 | G. Silahtaroğlu, Veri Madenciliği, Papatya Yayınevi, İstanbul, 2012 | ||
| 3 | Dr.Yalçın Özkan, Veri Madenciliği Yöntemleri, Papatya Yayınevi,İstanbul, 2012 | ||
| 4 | J.Han, M.Kamber, Data.Mining.Concepts.and.Techniques.2nd.Ed., America, 2010 |
Ölçme ve Değerlendirme Sistemi
| # | Ağırlık | Çalışma Türü | Çalışma Adı |
|---|---|---|---|
| 1 | 0.4 | 1 | 1. Mid-Term Exam |
| 2 | 0.2 | 9 | 1. Mid-Term Project |
| 3 | 0.4 | 5 | Final Exam |
Dersin Öğrenim Çıktıları ve Program Yeterlilikleri ile İlişkileri
| # | Öğrenim Çıktıları | Program Çıktıları | Ölçme ve Değerlendirme |
|---|---|---|---|
| 1 | Describe the definition and aim of data mining. | 7 | 1 |
| 2 | Identify overview to application areas, techniques and methods of discipline. | 1 | 1 |
| 3 | Identify data preprocessing steps for data warehouse concept. | 4͵7 | 1 |
| 4 | Plan proper sollution to the problems and define data mining algorithms. | 1 | 1͵2 |
| 5 | Apply association rules. | 1 | 1͵2 |
| 6 | Analyse desicion trees and classification techniques. | 1 | 2͵3 |
| 7 | Distinguish clustering methods. | 1͵7 | 3 |
| 8 | Plan a sample warehouse with a real world data and analyse this data with all data mining algorithms. | 1͵7 | 3 |
Not: Ölçme ve Değerlendirme sütununda belirtilen sayılar, bir üstte bulunan Ölçme ve Değerlerndirme Sistemi başlıklı tabloda belirtilen çalışmaları işaret etmektedir.
İş Yükü Detayları
| # | Etkinlik | Adet | Süre (Saat) | İş Yükü |
|---|---|---|---|---|
| 0 | Course Duration | 14 | 3 | 42 |
| 1 | Course Duration Except Class (Preliminary Study, Enhancement) | 14 | 2 | 28 |
| 2 | Presentation and Seminar Preparation | 0 | 0 | 0 |
| 3 | Web Research, Library and Archival Work | 0 | 0 | 0 |
| 4 | Document/Information Listing | 0 | 0 | 0 |
| 5 | Workshop | 0 | 0 | 0 |
| 6 | Preparation for Midterm Exam | 1 | 25 | 25 |
| 7 | Midterm Exam | 1 | 1 | 1 |
| 8 | Quiz | 0 | 0 | 0 |
| 9 | Homework | 0 | 0 | 0 |
| 10 | Midterm Project | 1 | 0 | 0 |
| 11 | Midterm Exercise | 0 | 0 | 0 |
| 12 | Final Project | 1 | 7 | 7 |
| 13 | Final Exercise | 0 | 0 | 0 |
| 14 | Preparation for Final Exam | 1 | 25 | 25 |
| 15 | Final Exam | 1 | 1 | 1 |
| 129 | ||||