Mining Ubiquitous and Social Environments (MUSE 2010)

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[3]  Sudipto Guha,et al.  Clustering data streams , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[4]  Gregory F. Cooper,et al.  The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks , 1989, AIME.

[5]  Abraham Silberschatz,et al.  Operating System Concepts , 1983 .

[6]  Cecilia M. Bitz,et al.  Arctic sea ice decline : observations, projections, mechanisms, and implications , 2008 .

[7]  Divesh Srivastava,et al.  Diamond in the rough: finding Hierarchical Heavy Hitters in multi-dimensional data , 2004, SIGMOD '04.

[8]  Gregory F. Cooper,et al.  A Bayesian Method for Constructing Bayesian Belief Networks from Databases , 1991, UAI.

[9]  Douglas G. Altman,et al.  Practical statistics for medical research , 1990 .

[10]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[11]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[12]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[13]  Fernando Díaz,et al.  Automatic parameter tuning with a Bayesian case-based reasoning system. A case of study , 2009, Expert Syst. Appl..

[14]  M. V. Valkenburg.  Network Analysis , 1964 .

[15]  Daniel Gatica-Perez,et al.  Discovering human routines from cell phone data with topic models , 2008, 2008 12th IEEE International Symposium on Wearable Computers.

[16]  Biplav Srivastava,et al.  Domain-Dependent Parameter Selection of Search-based Algorithms Compatible with User Performance Criteria , 2005, AAAI.

[17]  Slava Kisilevich,et al.  Analysis of privacy in online social networks of runet , 2010, SIN.

[18]  Longbing Cao,et al.  Agent Mining: The Synergy of Agents and Data Mining , 2009, IEEE Intelligent Systems.

[19]  Divesh Srivastava,et al.  Finding Hierarchical Heavy Hitters in Data Streams , 2003, VLDB.

[20]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[21]  Jakob Eg Larsen,et al.  Mobile Context Toolbox , 2009, EuroSSC.

[22]  Frank Harary,et al.  Graph Theory , 2016 .

[23]  Anabel Quan-Haase,et al.  Information revelation and internet privacy concerns on social network sites: a case study of facebook , 2009, C&T.

[24]  Bryan Cantrill,et al.  Dynamic Instrumentation of Production Systems , 2004, USENIX Annual Technical Conference, General Track.

[25]  Rajeev Motwani,et al.  Maintaining variance and k-medians over data stream windows , 2003, PODS.

[26]  S. R. Subramanya,et al.  Enhancing the User Experience in Mobile Phones , 2007, Computer.

[27]  Jürgen Schmidhuber,et al.  Learning dynamic algorithm portfolios , 2006, Annals of Mathematics and Artificial Intelligence.

[28]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[29]  Nicol N. Schraudolph,et al.  Conjugate Directions for Stochastic Gradient Descent , 2002, ICANN.

[30]  G. Bell,et al.  A digital life , 2007 .

[31]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

[32]  Shashi Shekhar,et al.  Spatial Databases: A Tour , 2003 .

[33]  Rossano Schifanella,et al.  Folks in Folksonomies: social link prediction from shared metadata , 2010, WSDM '10.

[34]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[35]  R. Kwok.  Personal technology: Phoning in data , 2009, Nature.

[36]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[37]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[38]  Johannes Gehrke,et al.  Mining data streams under block evolution , 2002, SKDD.

[39]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[40]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[41]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[42]  Jingjing Lu,et al.  Comparing naive Bayes, decision trees, and SVM with AUC and accuracy , 2003, Third IEEE International Conference on Data Mining.

[43]  James A. Landay,et al.  The Mobile Sensing Platform: An Embedded Activity Recognition System , 2008, IEEE Pervasive Computing.

[44]  Yifan Li,et al.  Clustering moving objects , 2004, KDD.

[45]  Padhraic Smyth,et al.  Trajectory clustering with mixtures of regression models , 1999, KDD '99.

[46]  Vittorio Loreto,et al.  Network properties of folksonomies , 2007, AI Commun..

[47]  Jason Martin,et al.  Ethno-Racial Identity Displays on Facebook , 2009, J. Comput. Mediat. Commun..

[48]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[49]  Jan Larsen,et al.  Estimating human predictability from mobile sensor data , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[50]  A. U.S.,et al.  Predictability , Complexity , and Learning , 2002 .

[51]  Andy Oram,et al.  Understanding the Linux Kernel, Second Edition , 2002 .

[52]  Koen Vanhoof,et al.  Research Challenges in Ubiquitous Knowledge Discovery , 2008, Next Generation of Data Mining.

[53]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[54]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[55]  M. Newman,et al.  Why social networks are different from other types of networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[56]  Nicole B. Ellison,et al.  Managing Impressions Online: Self-Presentation Processes in the Online Dating Environment , 2006, J. Comput. Mediat. Commun..

[57]  Ricardo A. Baeza-Yates,et al.  Extracting semantic relations from query logs , 2007, KDD '07.

[58]  Philip S. Yu,et al.  A framework for resource-aware knowledge discovery in data streams: a holistic approach with its application to clustering , 2006, SAC '06.

[59]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[60]  David Roberts,et al.  MobSens: Making Smart Phones Smarter , 2009, IEEE Pervasive Computing.

[61]  J. Fleiss.  Statistical methods for rates and proportions , 1974 .

[62]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[63]  Yun Gao,et al.  Estimating the Entropy of Binary Time Series: Methodology, Some Theory and a Simulation Study , 2008, Entropy.

[64]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[65]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[66]  Chuanyi Ji,et al.  Proactive network fault detection , 1997, Proceedings of INFOCOM '97.

[67]  Dino Pedreschi,et al.  Time-focused clustering of trajectories of moving objects , 2006, Journal of Intelligent Information Systems.

[68]  Peter Brusilovsky,et al.  From adaptive hypermedia to the adaptive web , 2002, CACM.

[69]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[70]  Simon Günter,et al.  A Stochastic Quasi-Newton Method for Online Convex Optimization , 2007, AISTATS.

[71]  Yuri M. Suhov,et al.  Nonparametric Entropy Estimation for Stationary Processesand Random Fields, with Applications to English Text , 1998, IEEE Trans. Inf. Theory.

[72]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[73]  Andrew McCallum,et al.  An Introduction to Conditional Random Fields for Relational Learning , 2007 .

[74]  Panos Kalnis,et al.  On Discovering Moving Clusters in Spatio-temporal Data , 2005, SSTD.

[75]  E A Leicht,et al.  Community structure in directed networks. , 2007, Physical review letters.

[76]  Wray L. Buntine.  A Guide to the Literature on Learning Probabilistic Networks from Data , 1996, IEEE Trans. Knowl. Data Eng..

[77]  Alessandro Acquisti,et al.  Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook , 2006, Privacy Enhancing Technologies.

[78]  J. Nocedal.  Updating Quasi-Newton Matrices With Limited Storage , 1980 .

[79]  Divesh Srivastava,et al.  Finding hierarchical heavy hitters in streaming data , 2008, TKDD.

[80]  Wei Chang,et al.  A stack-based prospective spatio-temporal data analysis approach , 2008, Decis. Support Syst..

[81]  Donato Malerba,et al.  Spatial Clustering of Structured Objects , 2005, ILP.

[82]  M. Gaber,et al.  Mobile Data Mining for Intelligent Healthcare Support , 2008 .

[83]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.