Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Improving text retrieval for the routing problem using latent semantic indexing
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering similar patterns in time series
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
SciQL: bridging the gap between science and relational DBMS
Proceedings of the 15th Symposium on International Database Engineering & Applications
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Similarity measure of time series is an important subroutine in many KDD applications. Previous similarity models mainly focus on the prominent series behaviors by considering the whole information of time series. In this paper, we address the problem: which portion of information is more suitable for similarity measure for the data collected from a certain field. We propose a model for the retrieval and representation of the partial information in time series data, and a methodology for evaluating the similarity measurements based on partial information. The methodology is to retrieve various portions of information from the raw data and represent it in a concise form, then cluster the time series using the partial information and evaluate the similarity measurements through comparing the results with a standard classification. Experiments on data set from stock market give some interesting observations and justify the usefulness of our approach.