Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques Mert Özer, Ilkcan Keles, Ismail Hakki Toroslu, Pinar Karagoz, Hasan Davulcu The Computer Journal 2015
Predicting the Location and Timeof Mobile Phone Users by Using Sequential Pattern Mining Techniques
Mert Özer, Ilkcan Keles, Ismail Hakki Toroslu, Pinar Karagoz, Hasan Davulcu
The Computer Journal 2015
CONTENTS Introduction Data & Problem Definition Proposed Methods Evaluation & Experimental Results Conclusion & Discussion
INTRODUCTIONLocation PredictionSequential Pattern MiningMotivation
MotivationMobile phone operator companies are eager to
know the location flow of their users to build more reasonable advertisement strategies. to build more reasonable base station installation
plans. can be used by city administrators to determine
mass people movement patterns around the city.
PROBLEM DEFINITIONSThree Sub-Problem Definitions of Broader Location
Prediction Problem Next Location and Time Prediction Using Spatio-
Temporal Data Next Location Change Prediction Using Spatial
Data Next Location Change and Time Prediction Using
Spatio-Temporal Data
Problem DefinitionNext Location and Time Prediction Using Spatio-
Temporal Data• to predict the location and the time of the
next action in the next time interval of the user• divide a day into time intevals• cluster base stations according to their
locations into regions
Training Data Definition ( Call Detail Data )Have 11 attributesbase station id#1, phone number#1, city plate#1,base station id#2, phone number#2, city plate#2, call time, cdr type, url, duration, call date.
The real data is obtained from one of the largest mobile
phone operators in Turkey.
Training DataThe data corresponds to an area roughly 25,000
km2 with a population around 5 million.Almost 70% of the population is concentrated in a
large urban area of approximately 1/3 of the region.
The data contains roughly 1 million users' log records for a period of 1 month.
The whole area contains 13281 base stations.
Method 1 - Next Location and Time Prediction Using Spatio-Temporal DataPreprocessingExtracting RegionsExtracting Frequent PatternsPrediction
Method 1 - PreprocessingThis paper filters unnecessary attributes.Daily call data records of each user are merged
into one row in a temporal order.Daily sequences structured as <base station id,
time of the day> pairs are created.
Method 1 - Preprocessing
Method 1 – Extracting RegionsUnder high number of base stations, it is not
practical to consider each as the center of movement and predict accordingly.
The paper clustered 13281 base stations into 100 regions by using K-Means algorithm.
Base station ids in the preprocessed data are replaced with the corresponding region ids in the daily sequences.
Method 1 – Extracting Regions
Extracted Regions
Method 1 – Extracting Frequent PatternsWork with four parameters;
• preprocessed training data• pattern length (the length of the desired
frequent pattern)• minimum support (the minimum ratio of the
pattern to occur in order to be identified as frequent)• time interval length (is used to discretize the
time of the day, defines the length of each interval)
Method 1 – Extracting Frequent PatternsThe method is very similar to AprioriAll
algorithm.Frequent pattern generation.
• The paper traverses the data to extract all candidate desired length patterns.• The ones that fall below the minimum support
threshold are eliminated.
Method 1 – Sample Frequent PatternsThree sample frequent patterns with the length 4
are presented below.
Test sequence is length of (k-1) and we want to predict kth element.
Then this (k-1) length pattern is searched in frequent pattern set.
If pattern starting with test sequence have been found, the last element of the matching pattern with the maximum support is generated as prediction.
Method 1 - Prediction
Method 1 - Prediction
Method 1 – Prediction – Time ToleranceDifficult to find exact matches between the current
user navigation sequence and existing frequent sequences.
Base station id and time interval pairs can be moved forward and backward in time with tolerance value.
Test instance: <(91,1015),(95,1230),(45,1630)> Frequent pattern set: {...,<(91,1000),(95,1245),(45,1630),(52,1700)>,...} Time tolerance value: 15 minutes Prediction: (52,1700)
This paper validated the results with real data obtained from one of the largest mobile phone operators in Turkey.
Results are very encouraging, and we have obtained very high accuracy results in predicting the next location change and time of users.
EVALUATION & RESULTS
Evaluation MetricsThis paper introduced 2 metrics to evaluate our
methods;
• g-accuracy: g-accuracy = • p-accuracy: p-accuracy=
The reason for using two different accuracy calculation is due to the fact that maybe there is no matching frequent pattern found for the queried instance.
This paper analyzes the effect of length of the frequent patterns and support threshold using the following parameter values.
• Pattern Length is 6• Minimum Support is 1.00E-6• Cluster Count is 100• Time Interval Length is 15 min• Time Tolerance is 75 min
Results of Method 1
Results – Pattern Length
When the pattern length increases, predicting g-accuracy decreases.
This is due to the fact that the number of longer frequent patterns is much fewer than the number of shorter frequent patterns.
Results – Minimum Support
Results – Minimum Support
When minimum support threshold value increases, prediction g-accuracy drops.
The reason for this result is that as minimum support threshold increases the number of generated frequent pattern decreases.
CONCLUSION & DISCUSSIONThis work shows that determining the
potential change of location of mobile phone users through sequential pattern mining techniques is possible with quite high accuracy.
This paper elaborated the effect of several factors such as pattern length tolerance and multi prediction limit and further improved the prediction performance.
Thank you !