Two algorithms for nearest-neighbor search in high dimensions
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Communications of the ACM
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces
SIAM Journal on Computing
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Contrast Plots and P-Sphere Trees: Space vs. Time in Nearest Neighbour Searches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The new Casper: query processing for location services without compromising privacy
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Some Effective Techniques for Naive Bayes Text Classification
IEEE Transactions on Knowledge and Data Engineering
Multi-class pattern classification using neural networks
Pattern Recognition
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Text classification: A least square support vector machine approach
Applied Soft Computing
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
Applied Soft Computing
Support feature machine for classification of abnormal brain activity
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Direct Discriminative Pattern Mining for Effective Classification
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Minimax-optimal classification with dyadic decision trees
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
In recent years, researchers have paid more and more attention on data mining of practical applications. Aimed to the problem of symptom classification of Chinese traditional medicine, this paper proposes a novel computing model based on the similarities among attributes of high dimension data to compute the similarity between any tuples. This model assumes data attributes as basic vectors of m dimensions and each tuple as a sum vector of all the attribute-vectors. Based on the transcendental concept similarity information among attributes, it suggests a novel distance algorithm to compute the similarity distance of any pair of attribute-vectors. In this method, the computing of similarity between any tuples are turned to the formulas of attribute-vectors and their projections of each other, and the similarity between any pair of tuples can be worked out by computing these vectors and formulas. This paper also presents a novel classification algorithm based on the similarity computing model and successfully applies the algorithm into the symptom classification of Chinese traditional medicine. The efficiency of the algorithm is proved by extensive experiments.