Temel Bileşenler Analizi Ve K-Ortalama Kümeleme Yönteminin Birlikte Kullanımı: Bir Örnek Uygulama- Combined Use Of Principal Component Analysis And K-Clustering Method: A Case Study
Abstract
Bu çalışmada, veri setlerinin kümelenmesi için kullanılan yöntemlerden biri olan K-ortalama yöntemi incelenmiştir. Buna istinaden büyük ölçekte verilen veri setlerini kümelemekte bir takım zorluklar yaşandığından ötürü boyut indirgemede yaygın olarak kullanılan Temel Bileşenler Analizi yöntemi kullanılmıştır. 3 farklı kümeye ayrılmak istenen veri seti için öncelikle, k-ortalama yöntemi uygulanmış olup, toplamdaki hata sayısı 16 olarak görülmüştür. Sonrasında temel bileşenler analizi kullanılarak boyut indirgenmiş ve böylelikle 16 olan hata sayısı 13’e düşmüştür.
References
- Alpar, Reha (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, 3. baskı
- Arthur, David & Vassilvitskii, Sergei (2007) k-means++: The Advantages of Careful Seeding
- Ding, Chris, He, Xiaofeng (2004), “K-means Clustering via Principal Component Analysis”, Proceedings of the 21st International Conference on Machine Learning, Banff, Canada.
- Hartigan, John., & Wang, M. (1979). A K-means clustering algorithm. Applied Statistics, 28, 100–108.
- Hatcher, Larry (1994), A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling, Cary, NC: The SAS Institute. Review pp. 325-339.
- Jang, Jyh-Shing Roger. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, (1997) “Neuro- fuzzy and soft computing: a computational approach to learning and machine intelligence”
- Jolliffe, I. (2002). Principal Component Analysis. Springer. 2nd edition.
- Lloyd, Sarah. (1957). Least squares quantization in pcm. Bell Telephone Laboratories Paper, Marray Hill.
- MacQueen, James. (1967). Some methods for classiŞcation and analysis of multivariate observations. Proc. 5th Berkeley Symposium, 281–297.
- Pinkowski, Brain, (1997). Principal component analysis of speech spectrogram images. Pattern Recogn, 30, 777–787.
- Ramsay, James, Munhall KG, Gracco VL, Ostry DJ. (1996). Functional data analyses of lip motion. J Acoust Soc Am., 99, 3718-3727.
Year 2016,
Volume: 8 Issue: 15, 85 - 94, 13.06.2016
Abstract
References
- Alpar, Reha (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, 3. baskı
- Arthur, David & Vassilvitskii, Sergei (2007) k-means++: The Advantages of Careful Seeding
- Ding, Chris, He, Xiaofeng (2004), “K-means Clustering via Principal Component Analysis”, Proceedings of the 21st International Conference on Machine Learning, Banff, Canada.
- Hartigan, John., & Wang, M. (1979). A K-means clustering algorithm. Applied Statistics, 28, 100–108.
- Hatcher, Larry (1994), A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling, Cary, NC: The SAS Institute. Review pp. 325-339.
- Jang, Jyh-Shing Roger. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, (1997) “Neuro- fuzzy and soft computing: a computational approach to learning and machine intelligence”
- Jolliffe, I. (2002). Principal Component Analysis. Springer. 2nd edition.
- Lloyd, Sarah. (1957). Least squares quantization in pcm. Bell Telephone Laboratories Paper, Marray Hill.
- MacQueen, James. (1967). Some methods for classiŞcation and analysis of multivariate observations. Proc. 5th Berkeley Symposium, 281–297.
- Pinkowski, Brain, (1997). Principal component analysis of speech spectrogram images. Pattern Recogn, 30, 777–787.
- Ramsay, James, Munhall KG, Gracco VL, Ostry DJ. (1996). Functional data analyses of lip motion. J Acoust Soc Am., 99, 3718-3727.
There are 11 citations in total.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | June 13, 2016 |
| Submission Date | September 12, 2014 |
| Published in Issue | Year 2016 Volume: 8 Issue: 15 |
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