Location Prediction of Mobile Phone Users Using Apriori-Based Sequence Mining with Multiple Support Thresholds

Keleş, Ilkcan; Özer, Mert; Toroslu, İsmail Hakkı; Karagöz, Pınar

doi:10.1007/978-3-319-17876-9_12

Cited by 11 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to our work, in [18], [19], [11] and [20], the authors propose sequential pattern mining techniques for the location prediction problem. In [18], Yavas et al propose an AprioriAll-based algorithm which is similar to our three methods.…”

Section: Related Workmentioning

confidence: 89%

See 1 more Smart Citation

Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

Özer

Keleş

Toroslu

et al. 2015

The Computer Journal

Self Cite

View full text Add to dashboard Cite

In recent years, using cell phone log data to model human mobility patterns became an active research area. This problem is a challenging data mining problem due to huge size and the non-uniformity of the log data, which introduces several granularity levels for the specification of temporal and spatial dimensions. This paper focuses on the prediction of the location of the next activity of the mobile phone users. There are several versions of this problem. In this work, we have concentrated on the following three problems: Predicting the location and the time of the next user activity, predicting the location of the next activity of the user when the location of the user changes, and predicting both the location and the time of the activity of the user when the user's location changes. We have developed sequential pattern mining based techniques for these three problems and validated the success of these methods with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, since we were able to obtain quite high accuracy results under a small prediction sets.

show abstract

Section: Related Workmentioning

confidence: 89%

“…In recent years, variety of location prediction schemes on human mobility have been studied in various dimensions [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [2], [21], [22].…”

Section: Related Workmentioning

confidence: 99%

Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

Özer

Keleş

Toroslu

et al. 2015

The Computer Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, in [8], we propose an Apriori-based method for location prediction which is named as Apriori-based Sequence Mining with Multiple Support Thresholds (ASMAMS). This method extract rules from data with respect to the multiple support parameters and predict accordingly.…”

Section: Previous Workmentioning

confidence: 99%

Predicting the next location change and time of change for mobile phone users

Özer

Keleş

Toroslu

et al. 2014

Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

Self Cite

View full text Add to dashboard Cite

Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.

show abstract

“…The limitation of this work was that the influence of the real time social ties was not put into consideration. Keles et al (2014), presented a research titled "Location Prediction of Mobile Phone Users using Apriori-Based Sequence Mining with Multiple Support Thresholds". The motivation was gotten from the need to provide better services and recommendations for mobile phone operators and smart city administration by using historical movement patterns for current location prediction of a person.…”

Section: IIImentioning

confidence: 99%

An Analysis of Location Prediction Models

S.¹,

Akinola²

2020

IJCA

View full text Add to dashboard Cite

Location Prediction is an estimate of a location in which a user will be at a particular place at a particular time within a certain probability. Location Prediction has gained prominence over the past decade which is due to improved technology in mobile communication. Classification of mobile users can be regular or random which can be used to ascertain the pattern of the user over a period of time which also helps in planning the movement of the user. This paper places emphasizes on the relevance of location prediction models in mobile users. A review of various location prediction model is carried out showing the effectiveness of each model, limitations, and also future work of some research works. Although this article does not give an exhaustive survey of all techniques and applications but it gives a description of several types of algorithms and models used for location prediction.

show abstract

Location Prediction of Mobile Phone Users Using Apriori-Based Sequence Mining with Multiple Support Thresholds

Cited by 11 publications

References 7 publications

Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

Predicting the Location and Time of Mobile Phone Users by Using Sequential Pattern Mining Techniques

Predicting the next location change and time of change for mobile phone users

An Analysis of Location Prediction Models

Contact Info

Product

Resources

About