『Abstract
　In order to develop quantitative seafloor sediment classification techniques it is important to acknowledge that by nature the boundaries between soft sediments are characterized by transition zones and therefore are indeterminate and gradual. A fuzzy clustering method, fuzzy c-means (FCM), was used to identify these transition zones within a subset of the data used to generate the Australian Seascapes classification model. The overlapping classes and gradual boundaries resulting from the fuzzy c-means algorithm provided estimates of sediment boundaries that are a closer model of reality than sharp boundaries. FCM output is given in the form of membership layers for each class, hard classes for each grid cell based on the maximum membership value, and a confusion index layer quantifying uncertainty in class attribution. The confusion index layer provided a spatial representation of transition zones and overlap between seafloor classes and highlighted areas if greatest uncertainty. We extended the standard FCM algorithm by applying the new FMLE fuzzy clustering algorithm that takes into account spatial relationships in the data. In addition, we implemented and applied new cluster validity techniques, PCAES, PBMF, and XB to determine the optimal number of clusters in the data, which is a novel pattern recognition application for seabed mapping. The 5-class FCM classification provided the most reliable result. The results of this research were tested and validated on a simulated dataset and then the clustering and validation algorithms were applied to marine sediment data to identify Seascapes. The new results were compared with previously published Seascapes classes identified with hard ISODATA clustering techniques from GeoScience Australia's Seascapes classification result. With the increasing use of physical surrogates to explain marine biodiversity, this research plays a crucial role in the development of techniques to identify habitat zones on the seabed.

Keywords: marine mapping; fuzzy boundaries; fuzzy c-means (FCM) clustering; cluster validity; uncertainty analysis』

1. Introduction
2. Methods
　2.1. Study area and data set
　2.2. Fuzzy c-means (FCM) classification
　2.3. Fuzzy maximum likelihood estimation (FMLE) clustering
　2.4. Spatial clustering
　2.5. Cluster validation methods
　2.6. Confusion in geographic boundary identification
　2.7. Simulation
3. Results
　3.1. Fuzzy c-means (FCM) Seascapes classification
4. Discussion
5. Conclusions
Acknowledgements
References