Block addressing indices for approximate text retrieval
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Scaling to domains with irrelevant features
Computational learning theory and natural learning systems: Volume IV
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Spatial join selectivity using power laws
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Self-similar and fractal nature of internet traffic
International Journal of Network Management
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Multifractal-based cluster hierarchy optimisation algorithm
International Journal of Business Intelligence and Data Mining
Spatial distance join based feature selection
Engineering Applications of Artificial Intelligence
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Feature selection, the process of selecting a feature subset from the original feature set, plays an important role in a wide variety of contexts such as data mining, machine learning, and pattern recognition. Recently, fractal dimension has been exploited to reduce the dimensionality of the data space. FDR(Fractal Dimensionality Reduction) is one of the most famous fractal dimension based feature selection algorithm proposed by Traina in 2000. However, it is inefficient in the high dimensional data space for multiple scanning the dataset. Take advantage of the Z-ordering technique, this paper proposed an optimized FDR, ZBFDR(Z-ordering Based FDR), which can select the feature subset through scanning the dataset once except for preprocessing. The experimental results show that ZBFDR algorithm achieves better performance.