SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART 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
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Optimizing time series discretization for knowledge discovery
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A decade of progress in indexing and mining large time series databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Discretization of Time Series Dataset with a Genetic Search
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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In this work, we propose an improved approach of time series data discretization using the Relative Frequency and K- nearest Neighbor functions called the RFknn method. The main idea of the method is to improve the process of determining the sufficient number of intervals for discretization of time series data. The proposed approach improved the time series data representation by integrating it with the Piecewise Aggregate Approximation (PAA) and the Symbolic Aggregate Approximation (SAX) representation. The intervals are represented as a symbol and can ensure efficient mining process where better knowledge model can be obtained without major loss of knowledge. The basic idea is not to minimize or maximize the number of intervals of the temporal patterns over their class labels. The performance of RFknn is evaluated using 22 temporal datasets and compared to the original time series discretization SAX method with similar representation. We show that RFknn can improve representation preciseness without losing symbolic nature of the original SAX representation. The experimental results showed that RFknn gives better term of representation with lower and comparable error rates.