The Classification of High Dimensional Indices for Spatial Data Similarity Search
نویسندگان
چکیده
The applications of spatial data similarity search are increasingly needed nowadays, and accordingly high dimensional index becomes one key technology to solve the problem of spatial data similarity search. Firstly, the distribution of high dimensional data is in-depth analyzed, and then high dimensional data retrieval for spatial data similarity search is also discussed. Secondly, based on the research, the classification of high dimensional indices for spatial data similarity search is presented, which initially makes a clear distinction of the relationship between the high dimensional index and the application of spatial data similarity search. Finally, the principle of high dimensional indices and the state of the applications in spatial data similarity search are analyzed with an example of typical index structure respectively, which lays a foundation for the research on index technology in spatial data similarity search. * [email protected]
منابع مشابه
A Monte Carlo-Based Search Strategy for Dimensionality Reduction in Performance Tuning Parameters
Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of ...
متن کاملAn improved opposition-based Crow Search Algorithm for Data Clustering
Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...
متن کاملOptimizing the Grade Classification Model of Mineralized Zones Using a Learning Method Based on Harmony Search Algorithm
The classification of mineralized areas into different groups based on mineral grade and prospectivity is a practical problem in the area of optimal risk, time, and cost management of exploration projects. The purpose of this paper was to present a new approach for optimizing the grade classification model of an orebody. That is to say, through hybridizing machine learning with a metaheuristic ...
متن کاملComparison of Machine Learning Algorithms for Broad Leaf Species Classification Using UAV-RGB Images
Abstract: Knowing the tree species combination of forests provides valuable information for studying the forest’s economic value, fire risk assessment, biodiversity monitoring, and wildlife habitat improvement. Fieldwork is often time-consuming and labor-required, free satellite data are available in coarse resolution and the use of manned aircraft is relatively costly. Recently, unmanned aeria...
متن کاملLarge-scale music similarity search with spatial trees
Many music information retrieval tasks require finding the nearest neighbors of a query item in a high-dimensional space. However, the complexity of computing nearest neighbors grows linearly with size of the database, making exact retrieval impractical for large databases. We investigate modern variants of the classical KD-tree algorithm, which efficiently index high-dimensional data by recurs...
متن کامل