@inproceedings{
    SocialContexts12,
    editor = "Hollm{\'e}n, Jaakko and Klawonn, Frank and Tucker, Allan",
    author = "Malmi, Eric and Raitio, Juha and Kohonen, Oskar and Lagus, Krista and Honkela, Timo",
    responsibleauthor = "Honkela, Timo",
    eventtime = "October 25-27",
    isbn = "978-3-642-34155-7",
    language = "eng",
    title = "Identifying Anomalous Social Contexts from Mobile Proximity Data Using Binomial Mixture Models",
    eventlocation = "Helsinki, Finland",
    country = "Finland",
    booktitle = "Advances in Intelligent Data Analysis XI",
    issn = "0302-9743",
    abstract = "Mobile proximity information provides a rich and detailed view into the social interactions of mobile phone users, allowing novel empirical studies of human behavior and context-aware applications. In this study, we apply a statistical anomaly detection method based on multivariate binomial mixture models to mobile proximity data from 106 users. The method detects days when a person's social context is unexpected, and it provides a clustering of days based on the contexts. We present a detailed analysis regarding one user, identifying days with anomalous contexts, and potential reasons for the anomalies. We also study the overall anomalousness of people's social contexts. This analysis reveals a clear weekly oscillation in the predictability of the contexts and a weekend-like behavior on public holidays.",
    juforank = "1",
    url = "http://link.springer.com/chapter/10.1007/978-3-642-34156-4_19",
    flags = "Tcogn Tsoci",
    il = "no",
    year = "2012",
    unitcode = "T306-100",
    impactfactor = "A4",
    pages = "195-206"
}