Clustering efficiency in a discrete time-series The 2019 Stack Overflow Developer Survey Results Are In Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsCan you use clustering to pick out signals in noisy data?Classify Customers based on 2 features AND a Time series of eventsClustering based on partial information?Sample selection through clusteringMultidimensional Dynamic Time Warping Implementation in Python - confirm?how to compare different sets of time series dataWhy use SOM for clustering?Difference between Time series clustering and Time series SegmentationWhat is an efficient clustering approach for grouping multivariate timeseries data into optimally persistent states?Clustering credit card accounts based on their balance trajectories
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Clustering efficiency in a discrete time-series
The 2019 Stack Overflow Developer Survey Results Are In
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsCan you use clustering to pick out signals in noisy data?Classify Customers based on 2 features AND a Time series of eventsClustering based on partial information?Sample selection through clusteringMultidimensional Dynamic Time Warping Implementation in Python - confirm?how to compare different sets of time series dataWhy use SOM for clustering?Difference between Time series clustering and Time series SegmentationWhat is an efficient clustering approach for grouping multivariate timeseries data into optimally persistent states?Clustering credit card accounts based on their balance trajectories
$begingroup$
Is it possible to identify the point in time where the cluster separation is at its most in a discrete time series clustering?
Say I have 4 clusters of discrete time series and I want to pick a point in time where I can tell with the least bias which cluster it belongs to after a kmeans clustering, what other criteria than classification success can U use to identify my cluster separation performance?
clustering time-series k-means
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
asked Apr 11 '16 at 5:22
WazaaWazaa
261
$endgroup$
bumped to the homepage by Community♦ 41 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Is it possible to identify the point in time where the cluster separation is at its most in a discrete time series clustering?
Say I have 4 clusters of discrete time series and I want to pick a point in time where I can tell with the least bias which cluster it belongs to after a kmeans clustering, what other criteria than classification success can U use to identify my cluster separation performance?
clustering time-series k-means
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
asked Apr 11 '16 at 5:22
WazaaWazaa
261
$endgroup$
bumped to the homepage by Community♦ 41 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
Is it possible to identify the point in time where the cluster separation is at its most in a discrete time series clustering?
Say I have 4 clusters of discrete time series and I want to pick a point in time where I can tell with the least bias which cluster it belongs to after a kmeans clustering, what other criteria than classification success can U use to identify my cluster separation performance?
clustering time-series k-means
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
asked Apr 11 '16 at 5:22
WazaaWazaa
261
$endgroup$
Is it possible to identify the point in time where the cluster separation is at its most in a discrete time series clustering?
Say I have 4 clusters of discrete time series and I want to pick a point in time where I can tell with the least bias which cluster it belongs to after a kmeans clustering, what other criteria than classification success can U use to identify my cluster separation performance?
clustering time-series k-means
clustering time-series k-means
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
asked Apr 11 '16 at 5:22
WazaaWazaa
261
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
asked Apr 11 '16 at 5:22
WazaaWazaa
261
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
edited Jan 12 '17 at 8:47
Kasra Manshaei
3,7991135
3,7991135
asked Apr 11 '16 at 5:22
WazaaWazaa
261
asked Apr 11 '16 at 5:22
WazaaWazaa
261
asked Apr 11 '16 at 5:22
WazaaWazaa
261
261
bumped to the homepage by Community♦ 41 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 41 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
1
active
oldest
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$begingroup$
Let me start with an important point; what other criteria than classification success can U use to identify my cluster separation performance?:
- Classification as an indicator for clustering performance has an internal paradox. If you have the classification the clustering question does not apply anymore. These two concept are coming form two totally different philosophies so I would say be careful if you have already understood the concepts (what you say may make sense in semi-supervised learning which is not in your tags so I assume there is a misunderstanding).
- Clustering has no performance evaluation! this is the problem I see most of data scientists struggling with. In practice you may define a good criterion (but honestly, what is good?!!) and deliver your solution, but in research schema there is no evaluation for clustering as the question itself is not well-defined i.e. you never have label to be sure who is who so you need to define closeness of points from which the problem starts; how close is called closeness?!!
- Be careful about Curse of Dimensionality while using k-means for time-series clustering (I'm not sure how you do it).
After these points let's have a look at your question.
What are time-series? If time-series are pretty non-stationary or simply speaking is the dynamic behind variation is complicated enough, then there is not a one-to-one map for time-series characteristics and time points (imagine a simple ECG signal. It's pretty simple but if you explore research community you will find super sophisticated methods for feature extraction on ECGs. I'm pretty confident finding a time point at which ECGs differ is almost impossible.). You may extract features from your time series or embed it into some n-dimensional manifolds and look at it. In best case you may find some time-related features which describe your time-series and you may find some time-related criteria at which time-series differ (however I'd say it's not likely to find them).
Assuming time-series are pretty well-behaved (!!) with a simple dynamic (should be super simple). Then a solution might be to define a distance function of time-series which outputs the pair-wise distances of all time-series as a single score. Then the maximum of this function returns the time-point at which these time-series are pretty distinguishable.
If you provide more information on your data I might be able to give a more detailed precise answer.
Good Luck!
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
$endgroup$
add a comment |
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$begingroup$
Let me start with an important point; what other criteria than classification success can U use to identify my cluster separation performance?:
- Classification as an indicator for clustering performance has an internal paradox. If you have the classification the clustering question does not apply anymore. These two concept are coming form two totally different philosophies so I would say be careful if you have already understood the concepts (what you say may make sense in semi-supervised learning which is not in your tags so I assume there is a misunderstanding).
- Clustering has no performance evaluation! this is the problem I see most of data scientists struggling with. In practice you may define a good criterion (but honestly, what is good?!!) and deliver your solution, but in research schema there is no evaluation for clustering as the question itself is not well-defined i.e. you never have label to be sure who is who so you need to define closeness of points from which the problem starts; how close is called closeness?!!
- Be careful about Curse of Dimensionality while using k-means for time-series clustering (I'm not sure how you do it).
After these points let's have a look at your question.
What are time-series? If time-series are pretty non-stationary or simply speaking is the dynamic behind variation is complicated enough, then there is not a one-to-one map for time-series characteristics and time points (imagine a simple ECG signal. It's pretty simple but if you explore research community you will find super sophisticated methods for feature extraction on ECGs. I'm pretty confident finding a time point at which ECGs differ is almost impossible.). You may extract features from your time series or embed it into some n-dimensional manifolds and look at it. In best case you may find some time-related features which describe your time-series and you may find some time-related criteria at which time-series differ (however I'd say it's not likely to find them).
Assuming time-series are pretty well-behaved (!!) with a simple dynamic (should be super simple). Then a solution might be to define a distance function of time-series which outputs the pair-wise distances of all time-series as a single score. Then the maximum of this function returns the time-point at which these time-series are pretty distinguishable.
If you provide more information on your data I might be able to give a more detailed precise answer.
Good Luck!
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
$endgroup$
add a comment |
$begingroup$
Let me start with an important point; what other criteria than classification success can U use to identify my cluster separation performance?:
- Classification as an indicator for clustering performance has an internal paradox. If you have the classification the clustering question does not apply anymore. These two concept are coming form two totally different philosophies so I would say be careful if you have already understood the concepts (what you say may make sense in semi-supervised learning which is not in your tags so I assume there is a misunderstanding).
- Clustering has no performance evaluation! this is the problem I see most of data scientists struggling with. In practice you may define a good criterion (but honestly, what is good?!!) and deliver your solution, but in research schema there is no evaluation for clustering as the question itself is not well-defined i.e. you never have label to be sure who is who so you need to define closeness of points from which the problem starts; how close is called closeness?!!
- Be careful about Curse of Dimensionality while using k-means for time-series clustering (I'm not sure how you do it).
After these points let's have a look at your question.
What are time-series? If time-series are pretty non-stationary or simply speaking is the dynamic behind variation is complicated enough, then there is not a one-to-one map for time-series characteristics and time points (imagine a simple ECG signal. It's pretty simple but if you explore research community you will find super sophisticated methods for feature extraction on ECGs. I'm pretty confident finding a time point at which ECGs differ is almost impossible.). You may extract features from your time series or embed it into some n-dimensional manifolds and look at it. In best case you may find some time-related features which describe your time-series and you may find some time-related criteria at which time-series differ (however I'd say it's not likely to find them).
Assuming time-series are pretty well-behaved (!!) with a simple dynamic (should be super simple). Then a solution might be to define a distance function of time-series which outputs the pair-wise distances of all time-series as a single score. Then the maximum of this function returns the time-point at which these time-series are pretty distinguishable.
If you provide more information on your data I might be able to give a more detailed precise answer.
Good Luck!
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
$endgroup$
add a comment |
$begingroup$
Let me start with an important point; what other criteria than classification success can U use to identify my cluster separation performance?:
- Classification as an indicator for clustering performance has an internal paradox. If you have the classification the clustering question does not apply anymore. These two concept are coming form two totally different philosophies so I would say be careful if you have already understood the concepts (what you say may make sense in semi-supervised learning which is not in your tags so I assume there is a misunderstanding).
- Clustering has no performance evaluation! this is the problem I see most of data scientists struggling with. In practice you may define a good criterion (but honestly, what is good?!!) and deliver your solution, but in research schema there is no evaluation for clustering as the question itself is not well-defined i.e. you never have label to be sure who is who so you need to define closeness of points from which the problem starts; how close is called closeness?!!
- Be careful about Curse of Dimensionality while using k-means for time-series clustering (I'm not sure how you do it).
After these points let's have a look at your question.
What are time-series? If time-series are pretty non-stationary or simply speaking is the dynamic behind variation is complicated enough, then there is not a one-to-one map for time-series characteristics and time points (imagine a simple ECG signal. It's pretty simple but if you explore research community you will find super sophisticated methods for feature extraction on ECGs. I'm pretty confident finding a time point at which ECGs differ is almost impossible.). You may extract features from your time series or embed it into some n-dimensional manifolds and look at it. In best case you may find some time-related features which describe your time-series and you may find some time-related criteria at which time-series differ (however I'd say it's not likely to find them).
Assuming time-series are pretty well-behaved (!!) with a simple dynamic (should be super simple). Then a solution might be to define a distance function of time-series which outputs the pair-wise distances of all time-series as a single score. Then the maximum of this function returns the time-point at which these time-series are pretty distinguishable.
If you provide more information on your data I might be able to give a more detailed precise answer.
Good Luck!
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
$endgroup$
Let me start with an important point; what other criteria than classification success can U use to identify my cluster separation performance?:
- Classification as an indicator for clustering performance has an internal paradox. If you have the classification the clustering question does not apply anymore. These two concept are coming form two totally different philosophies so I would say be careful if you have already understood the concepts (what you say may make sense in semi-supervised learning which is not in your tags so I assume there is a misunderstanding).
- Clustering has no performance evaluation! this is the problem I see most of data scientists struggling with. In practice you may define a good criterion (but honestly, what is good?!!) and deliver your solution, but in research schema there is no evaluation for clustering as the question itself is not well-defined i.e. you never have label to be sure who is who so you need to define closeness of points from which the problem starts; how close is called closeness?!!
- Be careful about Curse of Dimensionality while using k-means for time-series clustering (I'm not sure how you do it).
After these points let's have a look at your question.
What are time-series? If time-series are pretty non-stationary or simply speaking is the dynamic behind variation is complicated enough, then there is not a one-to-one map for time-series characteristics and time points (imagine a simple ECG signal. It's pretty simple but if you explore research community you will find super sophisticated methods for feature extraction on ECGs. I'm pretty confident finding a time point at which ECGs differ is almost impossible.). You may extract features from your time series or embed it into some n-dimensional manifolds and look at it. In best case you may find some time-related features which describe your time-series and you may find some time-related criteria at which time-series differ (however I'd say it's not likely to find them).
Assuming time-series are pretty well-behaved (!!) with a simple dynamic (should be super simple). Then a solution might be to define a distance function of time-series which outputs the pair-wise distances of all time-series as a single score. Then the maximum of this function returns the time-point at which these time-series are pretty distinguishable.
If you provide more information on your data I might be able to give a more detailed precise answer.
Good Luck!
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
answered Jan 12 '17 at 9:14
Kasra ManshaeiKasra Manshaei
3,7991135
3,7991135
add a comment |
add a comment |
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