Ten lectures on wavelets
Efficient retrieval for browsing large image databases
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
A comparison of DFT and DWT based similarity search in time-series databases
Proceedings of the ninth international conference on Information and knowledge management
Segment-based approach for subsequence searches in sequence databases
Proceedings of the 2001 ACM symposium on Applied computing
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
IEEE Computational Science & Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
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
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Similarity Search Over Time-Series Data Using Wavelets
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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
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The field of time series data mining has seen an explosion of interest in recent years. This interest has flowed over into many applications areas, including fiber manufacturing systems. The volume of time series data generated by a fiber monitoring system can be huge. This limits the applicability of data mining algorithms to this problem domain. A widely used solution is to reduce the data size through feature extraction. Four of the mostly commonly used feature extraction techniques are Fourier transforms, Wavelets, Piecewise Aggregate Approximation, and Piecewise Linear Approximation (PLA). In this paper, we first empirically demonstrate that PLA techniques produce the highest quality features for this problem domain. We then introduce a novel PLA algorithm that is shown to produce higher quality features than any other currently available techniques.