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In data analysis, the self-similarity matrix is a graphical representation of similar sequences in a data series. Similarity can be explained by different measures, like spatial distance (distance matrix), correlation, or comparison of local histograms or spectral properties (e.g. IXEGRAM). This technique is also applied for the search of a given pattern in a long data series as in . A similarity plot can be the starting point for dot plots or recurrence plots.

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  • In data analysis, the self-similarity matrix is a graphical representation of similar sequences in a data series. Similarity can be explained by different measures, like spatial distance (distance matrix), correlation, or comparison of local histograms or spectral properties (e.g. IXEGRAM). This technique is also applied for the search of a given pattern in a long data series as in . A similarity plot can be the starting point for dot plots or recurrence plots. (en)
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  • In data analysis, the self-similarity matrix is a graphical representation of similar sequences in a data series. Similarity can be explained by different measures, like spatial distance (distance matrix), correlation, or comparison of local histograms or spectral properties (e.g. IXEGRAM). This technique is also applied for the search of a given pattern in a long data series as in . A similarity plot can be the starting point for dot plots or recurrence plots. (en)
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  • Self-similarity matrix (en)
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