标题： 用于挖掘移动电话用户行为的个性化时间序列分割 显示英文标题

标题： Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior

摘要： 移动电话可以记录个人的日常行为数据，作为时间序列。 本文提出了一种有效的时间序列分割技术，利用个人的移动电话数据提取具有相似行为特征的最佳时间片段。 一个人的行为决定因素之一是在一天中的不同时间和一周中的不同天所从事的各种活动。 在许多情况下，这种行为会遵循时间模式。 目前，研究人员使用基于相等间隔或不相等间隔的时间分割来挖掘移动电话用户的行为。 大多数方法考虑了全天24小时的静态时间覆盖范围，很少有方法考虑时间序列数据中的事件数量。 然而，由于没有考虑到一周内个人行为的多样性，这些分割不一定映射到个人用户的活动和随后的行为模式。 因此，我们提出了一个以行为为导向的时间分割（BOTS）技术，不仅考虑一周的时间覆盖范围，还动态考虑时间序列数据中多样化行为的数量，以便在一周内生成具有相似行为的时间片段。 在真实移动电话数据集上的实验表明，我们提出的分割技术更好地捕捉了用户在一天中和一周中不同时间的主要行为，从而能够生成高置信度的时间规则，以挖掘个人移动电话用户的行为。 显示英文摘要

摘要： 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-h-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.