Today, I selected figures for defense.
Summary:
I sent you by an email.
Tomorrow, I will start to write the thesis.
3/29/2018
3/28/2018
analyze 15 logs
Today, I analyze 15 logs
Summary:
The result is also not good for all models.
Tomorrow, I will try different number of logs and start to write the thesis.
Summary:
The result is also not good for all models.
Tomorrow, I will try different number of logs and start to write the thesis.
3/27/2018
add SOM
Today, I added SOM.
Summary:
For now, I just cluster sonic log, namely DTC and DTS because it is visible. From the scatter plot, we can see that sonic log is better to be divided into 2 or 3 clusters, which are close to prediction performance levels. As a result, the second and third figures are plotted after comparing prediction performance with 4 clustering models. However, some of them are close to prediction results while others are not.
I think that is the best that I can do if only sonic log is included for clustering.
Sonic plot:
Two clusters:
Three clusters:
Tomorrow, I will include some inputs into these models to see if more clusters can be determined and if results could be improved.
Summary:
For now, I just cluster sonic log, namely DTC and DTS because it is visible. From the scatter plot, we can see that sonic log is better to be divided into 2 or 3 clusters, which are close to prediction performance levels. As a result, the second and third figures are plotted after comparing prediction performance with 4 clustering models. However, some of them are close to prediction results while others are not.
I think that is the best that I can do if only sonic log is included for clustering.
Sonic plot:
Two clusters:
Three clusters:
Tomorrow, I will include some inputs into these models to see if more clusters can be determined and if results could be improved.
3/26/2018
clustering models
Today, I built three clustering models for sonic log.
Summary:
Three models are:
1. k-means clustering
2. density-based spatial clustering of applications with noise (DBSCAN)
3. expectation maximization algorithm for gaussian mixture model (EM GMM)
The following are results of two clusters and three clusters.
Two clusters
Three clusters
From two figures, I think that two clusters performs better than three clusters. For k-means, it includes medium and poor clusters together and for DBSCAN and EM GMM, they include good and medium clusters together.
Tomorrow, I will try to improve the results.
Summary:
Three models are:
1. k-means clustering
2. density-based spatial clustering of applications with noise (DBSCAN)
3. expectation maximization algorithm for gaussian mixture model (EM GMM)
The following are results of two clusters and three clusters.
Two clusters
Three clusters
From two figures, I think that two clusters performs better than three clusters. For k-means, it includes medium and poor clusters together and for DBSCAN and EM GMM, they include good and medium clusters together.
Tomorrow, I will try to improve the results.
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