Karma model tabanlı kümeleme.

TEZ10522Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2017.Kaynakça (s. 109-120) var.xx, 136 s. : res. (bzs. rnk.), tablo ; 29 cm.Bu çalı¸sma, bir popülasyondaki karma¸sık heterojenlik özelliklerini ara¸stıran kümeleme tekniklerine dayalı karma modellerin incelenmesi üze-rinde yo?gunla¸smı¸stır. Hem parametrik hem de parametrik olmayan karma modeller farklı durumlarda incelenmi¸stir. Özellikle Poisson karma regresyon modelleri ve Sıfır ¸si¸sirilmi¸s Poisson karma modelleri parametrik durum için incelenirken Gauss çekirdek yo?gunlukları da parametrik olmayan durum için incelenmi¸stir. Parametrik karmalar, kümeleri ve bile¸senleri aynı varsayarken, bunlar parametrik olmayan durum için voronoi diagramları ve sonsuz bile¸sen modelleri aracılı?gı ile grafik teori teknikleri kullanılarak farklı ¸sekilde gözlemlendi. Karma modeller kullanılan daha iyi kümeleme yöntemleri kümeleme analizi yolu ile sınıflandırılarak belirlendi. Sonuçlara göre, parametrik ve parametrik olmayan karmalardaki kümelerin sayıları aynı olmalarına ra?gmen bu yakla¸sımlar genellikle çok farklıdır. Parametrik olmayan karı¸sılar, sınıflandırıcı performansını arttrıma ve dolayısıyla heterojeli?gi daha verimli bir ¸sekilde sıkı¸stırma yenetenekler ile kanılandı?gı üzere daha iyi kümeler üretir. Bu nedenle, karma modellere, geleneksel kümeleme yönetemleri üzerindeki yo?gunluklar ve parametreler yoluyla küme özelliklerinde çkarımlara izin verme yetene?gi onları daha iyi kümeleme yönetemleri haline getirir. Parametrik olmayan karma daha sa?glam, esnek ve daha iyi kümeler ürettikleri için parametrik olarak tercih edilmelidir.The study focused on exploring the use of mixture models for data clustering techniques on data with complex heterogeneous properties. Both parametric and nonparametric mixture models were here considered. In particular, Poisson mixture regression models and Zero Inflated Poisson mixture models were applied and analysed for the parametric case as mixtures of Gaussian kernel densities were considered for the nonparametric case. Although, parametric mixtures assume clusters and components to be the same, these are observed different by either using graph theory techniques via voronoi diagrams or infinite componential models in the nonparametric case. Determination of better clustering method using mixture models was enabled through classification via clustering analysis. Results shows that although number of clusters from the parametric mixtures can be the same with nonparametric, these approaches are generally very different. Nonparametric mixtures produce better clusters as evidenced by their ability to improve classifier performance and hence compacting heterogeneity in a more efficiently way whilst parametric are a bit rigid. The ability by mixture models to allow inferences on cluster properties via densities and parameters over traditional clustering methods makes them a better clustering methods. We conclude that although mixture models are better than hierarchical models, nonparametric mixture should be preferred over parametric as they are more robust, flexible and produce beter clusters

Poisson Mixture Regression Models for Heart Disease Prediction

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model

Assessing the Effects of Estrogen on the Dynamics of Breast Cancer

Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer