1. Modelling Accessibility Performance in LTE
networks, An Analytics Methodology
May 6th
. 2015 / Mingxin Guan

2. OUTLINE
Background
Data Pre-processing
Cluster Analysis
Prediction
Evaluation
Conclusion

3. Background

4. Data Pre-processing
Sampledatareview

5. Data Pre-processing
Feature Selection
Data variance
Laplacian score

6. Data Pre-processing
FeatureSelection

7. Data Pre-processing

8. Cluster Analysis
K-means++
K-means Agglomerative
Clustering
With
Single Linkage
Average Linkage
Complete Linkage

9. Cluster Analysis
K-means++
•Randomly select a point from the set of data
points entered as the first cluster centroid.
•For each set of data points x, calculate its
distance to the nearest cluster centroids
•Select a new data point as the new cluster
centroid, the principle choice is the more large
distance point
•Repeat until k cluster centroids have been
chosen
•Now that the initial centroids have been
chosen, proceed using standard k-means
clustering.

10. Cluster Analysis
•Assign each sample in each cluster.
•Compute distance between two clusters
•Combine the two nearest clusters
•Repeat, until all samples are assigning into
one cluster.
Agglomerative
Clustering

11. Cluster Analysis
•Single Linkage
•Complete Linkage
•Average Linkage
Distance between
clusters
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12. Cluster Analysis

13. Cluster Analysis

14. Cluster Analysis

15. Prediction
Markov
Chains
•Network KPI state space
S = {99%~100%, 95%~99%, 90%~95%,
85%~90%, 80%~85%, <80%}
S = {1,2,3,4,5,6}
•Transition probability matrix (T)
•Initial state probability vector (P0)

16. Prediction
The observation
level in each time-
section

17. Prediction
•Predicted state probability vector (P1)
P1 = P0*T
•Result of prediction

18. Evaluation
Accuracy of prediction

19. Conclusion
Merged two methods for feature selection in
unsupervised learning
Four clustering models
Predicting by Markov chains
Evaluating clustering models

20. Thank you!