document: Similarity measure based on partial information of time series 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 .
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