# Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
## 1 Introduction  
According to the Korean Statistical Information Service [1], in 2016, the number of mobile phone subscriptions worldwide had surpassed the world's population by reaching 7.5 billion subscriptions; the number of mobile subscribers in Korea alone had exceeded 60 million. Furthermore, based on this survey, the risks of smartphone addiction are increasing every year. Owing to the increase in the use of smartphones, the issue of smartphone addiction has become a serious social problem; for example, the emergence of \"Smombies\" (Smombie is a compound word including smart phone and zombie; it refers to individuals who walk on the road, while looking at their smartphone; because they are immersed in their smart phones, and are not aware of the surrounding environment, and such walking leads to a high risk of accidents.) has taken place. Even with the large number of studies performed in Korea, there is still no appropriate method to solve this addiction problem [2,3].  
In particular, the data provided by the Korea National Statistical Office (KNSO) and various other studies are primarily based on old research methods such as questionnaires and interviews; these methods lead to inferior analysis results, because they are based on analyses and evaluation of the self-reported data, which can be intentionally manipulated.  
Therefore, in recently years, studies using data collected via apps were conducted to analyze patterns of smartphone addiction; however, this method has several restrictions based on the software development kit (SDK) used to develop the app in terms of the analysis of the usage pattern of theapp. Nevertheless, using the new approach developed by [PERSON] [4], the phone usage pattern can be easily analyzed by measuring the usage time of the smartphone, time spent using apps by category, and number of times the apps were executed. Furthermore, [PERSON] [5] compared self-reporting and smartphone usage methods and found that there is a significant cognitive bias between the results of the two techniques. [PERSON] [6] concluded that smartphone use should be revised to some extent as a measure of addiction, and that smartphone use also plays a role of innocence.  
Thus, in this study, we combine both abovementioned approaches; in particular, we use data mining technology to collect data collected via self-reports using a smartphone app called \"How often do you use\" [7]. The data mining techniques were used to study the difference in perception and behavior of smartphone use as well as the effect of addiction on learning. In summary, the objectives of this study are as follows:  
* Understanding the pattern of smartphone usage by collecting long-term smartphone usage data in a manner that cannot be intentionally manipulated;
* Identifying the impact of smartphone addiction in a reliable manner;
* Identifying the differences between the self-reported data on smartphone usage.  
The smartphone industry is growing rapidly with ever increasing options that influence users to follow through with newer adoptions and increased usage. Thus, by analyzing smartphone usage and addiction via unconscious usage pattern collection and data mining techniques rather than the existing survey methods, we can find methods to utilize smartphones efficiently and effectively.