Titlebook: Behavior Computing; Modeling, Analysis, Longbing Cao,Philip S. Yu Book 2012 Springer-Verlag London 2012 Behavior Impact Analysis.Behavior

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Clustering Clues of Trajectories for Discovering Frequent Movement Behaviorser. In addition to spatial and temporal biases, we observe that trajectories contain ., i.e., the time durations when no data points are available to describe movements of users, which bring many challenge issues in clustering trajectories. We claim that a movement behavior would leave some . in its

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Linking Behavioral Patterns to Personal Attributes Through Data Re-Miningvior pattern analysis. This study presents such a methodology, that can be converted into a decision support system, by the appropriate integration of existing tools for association mining and graph visualization. The methodology enables the linking of behavioral patterns to personal attributes, thr

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Mining Causality from Non-categorical Numerical Dataa, most of the times causality is difficult to detect and measure. In fact, considering two time series, although it is possible to measure the correlation between both associated variables, correlation metrics don’t show the cause-effect direction and then, . and . variables are not identified by t

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A Fast Algorithm for Mining High Utility Itemsetsequent itemset may not be the itemset with high value. High utility itemset mining considers both of the profits and purchased quantities for the items, which is to find the itemsets with high utility for the business. The previous approaches for mining high utility itemsets first apply frequent ite

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Individual Movement Behaviour in Secure Physical Environments: Modeling and Detection of Suspicious entially suspicious actions, data about the movement of users can be captured through the use of RFID tags and sensors, and patterns of suspicious behaviour detected in the captured data. This chapter presents four types of suspicious behavioural patterns, namely temporal, repetitive, displacement a

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https://doi.org/10.1007/978-94-011-7633-0ever, research on its application to incorporate personalization in generalized software packages is rare. In this paper, we use a semi-Markov model to dynamically display personalized information in the form of high-utility software functions (states) of a software package to a user. We develop a d