calcCompDist: Formula for the Complexity-Based Dissimilarity
In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series
Description
Usage
Arguments
Value
References
Description
Method which contains the formula used in compDist and
compDistTSList. Considering the complexity to be a kind of
entropy measure, our formula is similar to the normalized Variation of
Information described by Meila (2003) and Wu, Xiong and Chen (2009), setting
the joint entropy in relation to the single entropies. This results in a value
from the interval [0,1], compared to (0.5,1] in the formula of Keogh et al.
(2007). Our measure is symmetric.
Usage
1
calcCompDist(xLength, yLength, xyLength, yxLength)
Arguments
xLength
Length of the first string/time series after compression.
yLength
Length of the second string/time series after compression.
xyLength
Length of the concatenation of first and second string/time
series after compression.
yxLength
Length of the concatenation of second and first string/time
series after compression.
Value
The dissimilarity as numeric from the range [0,1].
References
Keogh, E., Lonardi, S., Ratanamahatana, C. A., Wei, L., Lee, S.-H. & Handley, J.
(2007). Compression-based data mining of sequential data. Data Mining
and Knowledge Discovery, 14(1), 99–129.
Li, M., Badger, J. H., Chen, X., Kwong, S., Kearney, P. & Zhang, H. (2001).
An information-based sequence distance and its application to whole
mitochondrial genome phylogeny. Bioinformatics, 17(2), 149–154.
Meila, M. (2003). Comparing clusterings by the variation of information. In
B. Schölkopf & M. K. Warmuth (Eds.), Learning theory and kernel machines:
16th annual conference on learning theory and 7th kernel workshop, colt/kernel
2003, washington, dc, usa, august 24-27, 2003. proceedings (pp. 173-187).
Springer Berlin Heidelberg.
Wu, J., Xiong, H. & Chen, J. (2009). Adapting the right measures for k-means
clustering. In Proceedings of the 15th acm sigkdd international conference
on knowledge discovery and data mining (pp. 877-886). ACM.
Jakob-Bach/FastTSDistances documentation built on May 13, 2019, 1:15 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value References
Description
Method which contains the formula used in compDist and
compDistTSList. Considering the complexity to be a kind of
entropy measure, our formula is similar to the normalized Variation of
Information described by Meila (2003) and Wu, Xiong and Chen (2009), setting
the joint entropy in relation to the single entropies. This results in a value
from the interval [0,1], compared to (0.5,1] in the formula of Keogh et al.
(2007). Our measure is symmetric.
Usage
1 | calcCompDist(xLength, yLength, xyLength, yxLength)
|
Arguments
xLength |
Length of the first string/time series after compression. |
yLength |
Length of the second string/time series after compression. |
xyLength |
Length of the concatenation of first and second string/time series after compression. |
yxLength |
Length of the concatenation of second and first string/time series after compression. |
Value
The dissimilarity as numeric from the range [0,1].
References
Keogh, E., Lonardi, S., Ratanamahatana, C. A., Wei, L., Lee, S.-H. & Handley, J. (2007). Compression-based data mining of sequential data. Data Mining and Knowledge Discovery, 14(1), 99–129.
Li, M., Badger, J. H., Chen, X., Kwong, S., Kearney, P. & Zhang, H. (2001). An information-based sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics, 17(2), 149–154.
Meila, M. (2003). Comparing clusterings by the variation of information. In B. Schölkopf & M. K. Warmuth (Eds.), Learning theory and kernel machines: 16th annual conference on learning theory and 7th kernel workshop, colt/kernel 2003, washington, dc, usa, august 24-27, 2003. proceedings (pp. 173-187). Springer Berlin Heidelberg.
Wu, J., Xiong, H. & Chen, J. (2009). Adapting the right measures for k-means clustering. In Proceedings of the 15th acm sigkdd international conference on knowledge discovery and data mining (pp. 877-886). ACM.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.