Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Exploring bit-difference for approximate KNN search in high-dimensional databases
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
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In this paper, we present continuous research on data analysis based on our previous work on similarity search problems. PanKNN[13] is a novel technique which explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively select data points which are closest to Q. It can be applied in various data mining fields. In this paper, we applied the Fuzzy concept to improve the performance of PanKNN, targeting the better decision making for the calculation of the distance between a data point and Q. This approach can assist to improve the performance of existing data analysis approaches.