Statistics

Clustering is an important analytical technique that has proven to affect human life positively through its application in cancer research, market segmentation, city planning etc. In this time of growing technological systems, mixed data has seen another face of longitudinal, directional and functional attributes which is worth paying attention to and analyzing. Previous research works on clustering relied largely on the inverse weight technique and B-spline in smoothing data and assessing the performance of various clustering algorithms. In 1971, Gower proposed a method of clustering for mixed variable types which has been extended to include functional and directional variables by Hendrickson (2014). In this study, we will do a comparative analysis of the performance of the hierarchical clustering mechanism using a simulated Functional data with mixed structure. We will adopt the Fourier basis smoothing procedure and use the Rand index (Rand 1971) and adjusted Rand index for the comparison of the various clustering algorithms.

Breast Cancer is a major disease affecting women's health in the United States with incidence and prevalence dominant among younger women and the Black race. We analyzed the association between breast cancer characteristics with age and race and how survival months and age differ in racial groups. Using the Surveillance, Epidemiology, and End Results (SEER) datasets we performed a logistic regression to examine significant predictors that affect survival month. There were 3414 whites, 291 Blacks, and 320 Others (American Indian/AK Native, Asian/Pacific Islander) in the sample. We found significant associations between racial groups and ages with significant differences in age between Blacks and Whites, and Whites and Others. Patients with a breast cancer tumor in grades 1 and 2 have higher survival months by (1.49 % and 0.49%) respectively.