Prediction of structured values using k nearest neighbours

In this work we are interested in prediction accuracy of nearest neighbour method for predicting structured values; multiclass classification and regression, hierarchical multilabel classification and short time series. Problems and techniques for dealing with this kind of data are presented. Prediction accuracy is tested on various datasets from various (but mostly enviromental) domains. For some cases we also check influence of different vote (distance) weighting schemes and feature weighting using Random Forest method. Method's accuracy is compared to predictive clustering rules and trees. We show that nearest neighbour method is capable of predicting structured data with accuracy comparable to trees and rules. Furtherwore, method is in some cases significantly better than rules. In our work we have only tested prediction with at most fifteen voting neighbours. We expect that method could perform even better when more neighbours are used. We implement three nearest neighbour search methods: simple search, kd tree and vp tree. All methods are compared regarding to time spent for searching in space (where instances are distributed according to random uniform distribution). We conclude that vp tree is faster than kd for high dimension spaces, but has also limitations (dimensionality cource) and simple search outperforms it for high-dimension spaces. For our implementations, we derive a simple (experiment-based) rule that decides which method to use according to number of instances, dimensions and voting neighbours. Implementation is done in Java as part of Clus - system for predictive clustering

The Impact of the Latest 3D Technologies on the Documentation of Underwater Heritage Sites

Documenting underwater cultural heritage is a challenging undertaking. Underwater environment is not a man’s natural habitat and special equipment and devices had to be invented so that he could enter and study this environment. Several decades of underwater research and many sacrifices were needed to fully understand the importance of underwater heritage and its protection. The means for accurate documentation underwater are very limited and demanding, due to required technical equipment it is also expensive. Emergence of modern 3D methods and accompanying software tools for processing of 3D data is therefore of utmost importance for documenting and protection of underwater cultural heritage. In comparison to manual and analog methods, 3D methods offer much better accuracy, they substantially shorten the necessary time spent underwater and in this way improve the safety at work as well as lower the entire cost of field work. For illustration of the above development we discuss archeological case studies from the North East Adriatic