A recent-biased dimension reduction technique for time series data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3518 LNAI, Issue , 2005, Pages 751-757

  Zhao, Yanchang ^a   Zhang, Chengqi ^a   Zhang, Shichao ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATABASE SYSTEMS; DISTANCE MEASUREMENT; KNOWLEDGE BASED SYSTEMS; PROBLEM SOLVING;

BIASED TECHNIQUE; DIMENSION REDUCTION; DWT;

TIME SERIES ANALYSIS;

EID: 26944460376     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11430919_87     Document Type: Conference Paper

References (8)

1. A. Bulut and A. K. Singh: SWAT: Hierarchical Stream Summarization in Large Networks. Proc. of the 19th Int. Conf. on Data Engineering, Bangalore, India, 2003.
2. E. Cohen and M. Strauss: Maintaining time-decaying stream aggregates. Proc. of the 22nd ACM Symposium on Principles of Database Systems, 2003.
3. T. Fu, F. Chung, V. Ng and R. Luk: Pattern Discovery from Stock Time Series Using Self-Organizing Maps. Workshop Notes of KDD'01. San Francisco, CA, USA, 2001.
4. E. Fink, K. B. Pratt, and H. S. Gandhi: Indexing of Time Series by Major Minima and Maxima. Proc. of the IEEE Int. Conf. on Systems, Man, and Cybernetics, 2003.
5. E. Keogh, K. Chakrabarti, et al: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems 3(3), 2000.
6. F. Mörchen: Time series feature extraction for data mining using DWT and DFT. In Technical Report No. 33, Philipps-University Marburg, 2003.
7. C.-S. Perng, H. Wang, et al: Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases. Proc. of the 16th ICDE, San Diego, CA, 2000.
8. W-G. Teng, M-S. Chen, and P.S. Yu: Using Wavelet-based Resource-aware Mining to Explore Temporal and Support Count Granularities in Data Streams. Proc. of the SIAM Int. Conf. on Data Mining, April 2004.