The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
Hierarchical state machine architecture for regular expression pattern matching
Proceedings of the 19th ACM Great Lakes symposium on VLSI
ICIIC '10 Proceedings of the 2010 First International Conference on Integrated Intelligent Computing
Optimal Coding of Vectorcardiographic Sequences Using Spatial Prediction
IEEE Transactions on Information Technology in Biomedicine
| Hi-index | 0.00 |
A novel method of "predicting" sitter case attribute value is presented in this paper. The method allows users to choose two attributes, seed and target attribute, and to predict the target attribute value of the forthcoming sitter case. The method first retrieves string sequences of the seed attribute according to filters the users set. Then, it finds the words in the sequences and calculates the term frequencies of the words. With the term frequencies, the proposed method uses vector space model to measure the similarity between the testing sequences and the benchmark sequence. At the end, the testing sequence which has highest Cosine similarity value is chosen and the filtering value the method uses to generate the testing sequence is the predicted result. These predicted results allow hospitals to adjust their strategies on resource assignments to better handle patient needs.