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

Özer, Mert; Keleş, Ilkcan; Toroslu, Hakki; Karagöz, Pınar; Davulcu, Hasan

doi:10.1093/comjnl/bxv075

Cited by 19 publications

(16 citation statements)

References 21 publications

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“…The reason is that such large segments may not be able to differentiate individual's diverse activities in a particular segment [40]. On the other hand, a number of researchers [25,31] use small interval based segmentation (e.g., 15 minutes) by taking into account the frequent variations of individual's behaviors. However, in many cases, these small interval based time segments may not be suitable to produce meaningful behavioral rules in terms of support value [40].…”

Section: Discretization Of Continuous Contextual Datamentioning

confidence: 99%

Research issues in mining user behavioral rules for context-aware intelligent mobile applications

Sarker

2018

Iran J Comput Sci

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Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build contextaware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules, a number of issues, such as the quality of smartphone data, understanding the relevancy of contexts, discretization of continuous contextual data, discovery of useful behavioral rules of individuals and their ordering, knowledge-based interactive post-mining for semantic understanding, and dynamic updating and management of rules according to their present behavior, are investigated. In this paper, we briefly discuss these issues and their potential solution directions for mining individuals' behavioral rules, for the purpose of building various context-aware intelligent mobile applications. We also summarize a number of real-life rule-based applications that intelligently assist individual smartphone users according to their behavioral rules in their daily activities.

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Section: Discretization Of Continuous Contextual Datamentioning

confidence: 99%

Research issues in mining user behavioral rules for context-aware intelligent mobile applications

Sarker

2018

Iran J Comput Sci

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“…As a result, each day is divided into a predefined number of equivalent length time intervals. For instance, Ozer et al [12] propose an approach to predict the location and time of mobile phone users by using sequential pattern mining techniques. In their approach, they use 15 minutes as a time interval length for segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…As our approach is individualized behaviororiented, the optimal base period to capture the behavioral pattern and corresponding optimal segments for producing temporal behavior rules vary from userto-user. if Applicability > A init then //store the base period as optimal base period 10 BP optimal ← BP //update initial applicability 11 A init ← Applicability //update optimal list 12 OSeg list ← updateOSegList(Seg list ) end //next base period 13 increase BP end 14 return OSeg list…”

Section: Identify Optimal Segmentationmentioning

confidence: 99%

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Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior

Sarker

Colman

Kabir

et al. 2017

The Computer Journal

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Mobile phones can record individual's daily behavioral data as a time-series. In this paper, we present an effective time-series segmentation technique that extracts optimal time segments of individual's similar behavioral characteristics utilizing their mobile phone data. One of the determinants of an individual's behavior is the various activities undertaken at various times-of-the-day and days-of-the-week. In many cases, such behavior will follow temporal patterns. Currently, researchers use either equal or unequal interval-based segmentation of time for mining mobile phone users' behavior. Most of them take into account static temporal coverage of 24-hours-a-day and few of them take into account the number of incidences in time-series data. However, such segmentations do not necessarily map to the patterns of individual user activity and subsequent behavior because of not taking into account the diverse behaviors of individuals over time-of-the-week. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that takes into account not only the temporal coverage of the week but also the number of incidences of diverse behaviors dynamically for producing similar behavioral time segments over the week utilizing time-series data. Experiments on the real mobile phone datasets show that our proposed segmentation technique better captures the user's dominant behavior at various times-of-the-day and days-of-the-week enabling the generation of high confidence temporal rules in order to mine individual mobile phone users' behavior.

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“…The best forecast accuracy is achieved with a model called HPY (Hierarchical Pitman-Yor) Prior Hour-Day Model, which is able to correctly localize the users with an accuracy of 50%. In [38] the urban cells are clustered together, in order to avoid repeated switches of users between adjacent cells and to force them to assume the same size of the rural cells. Successively, a data mining procedure is performed on the sequences of the movements, aiming to predict whether a user will change its position or not.…”

Section: Work On Call Data Recordsmentioning

confidence: 99%

Identifying user habits through data mining on call data records

Bianchi

Rizzi

Sadeghian

et al. 2016

Engineering Applications of Artificial Intelligence

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In this paper we propose a framework for identifying patterns and regularities in the pseudoanonymized Call Data Records (CDR) pertaining a generic subscriber of a mobile operator. We face the challenging task of automatically deriving meaningful information from the available data, by using an unsupervised procedure of cluster analysis and without including in the model any apriori knowledge on the applicative context. Clusters mining results are employed for understanding users' habits and to draw their characterizing profiles. We propose two implementations of the data mining procedure; the first is based on a novel system for clusters and knowledge discovery called LD-ABCD, capable of retrieving clusters and, at the same time, to automatically discover for each returned cluster the most appropriate dissimilarity measure (local metric). The second approach instead is based on PROCLUS, the well-know subclustering algorithm. The dataset under analysis contains records characterized only by few features and, consequently, we show how to generate additional fields which describe implicit information hidden in data. Finally, we propose an effective graphical representation of the results of the data-mining procedure, which can be easily understood and employed by analysts for practical applications.

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

Cited by 19 publications

References 21 publications

Research issues in mining user behavioral rules for context-aware intelligent mobile applications

Research issues in mining user behavioral rules for context-aware intelligent mobile applications

Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior

Identifying user habits through data mining on call data records

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