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.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-5517 |
| Date | 01 December 2021 |
| Creators | Amartey, Ishmael |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
Page generated in 0.0014 seconds