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2 P Angelov, An Approach for Fuzzy Rule-Base Adaptation Using On-line Clustering, International Journal of Approximate Reasoning, 35, 3, 2004, 275 289 3 B Babcock, S Babu, M Datar, R Motwani, J Widom, Models and Issues in Data Stream Systems Proc of 21st ACM Symposium on Principles of Database Systems (PODS 2002), Madison, Wisconsin, 2002, 30 45 4 S Bandyopadhyay, C Giannella, U Maulik, H Kargupta, K Liu, S Datta, Clustering Distributed Data Streams in Peer-to-Peer Environments, Information Sciences, 176, 2006, 1952 1985 5 J Beringer, E Hullermeier, Online Clustering of Parallel Data Streams, Data & Knowledge Engineering, 58, 2006, 180 204 6 J C Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms New York: Plenum Press, 1981 7 F Crespo, R Weber, A Methodology for Dynamic Data Mining Based on Fuzzy Clustering, Fuzzy Sets & Systems, 150, 2005, 267 284 8 S Guha, A Meyerson, N Mishra, R.

receivers and wishes to communicate (perhaps different) information simultaneously to each of the receivers This is the broadcast channel Finally, what if one has an arbitrary number of senders and receivers in an environment of interference and noise What is the capacity region of achievable rates from the various senders to the receivers This is the general network information theory problem All of the preceding problems fall into the general area of multiple-user or network information theory Although hopes for a comprehensive theory for networks may be beyond current research techniques, there is still some hope that all the answers involve only elaborate forms of mutual information and relative entropy Ergodic theory The asymptotic equipartition theorem states that most sample n-sequences of an ergodic process have probability about 2 nH and that there are about 2nH such typical sequences Hypothesis testing.

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Location-based services (Continued ) proximity, 454, 457 459 scene analysis, 450 454 triangulation, 447 450 location estimation media infrared (IR), 445 446, 457 radiofrequency (RF), 443 445 ultrasound, 446 447, 455 457 location estimation system de ned, 440 indoor, 454 459 outdoor, 459 464 location format transformation (LFT), 464 465 location sensor infrastructure, 440 location taxonomy absolute location, 442 physical location, 441 relative location, 442 symbolic location, 441 442 Location Interoperability Forum, 264 Location service system (LCS), 465 467 Log analyses, workload characterization, 161 163, 172 174 Logical key hierarchy (LKH), digital rights management, 401 Lookahead window, 118 Loose coupling, 319 320 Lorax, 98 Loss detection delay, 284 Loss rate, 107 Lotus/Lotus Notes, 261, 263 Low-end mobile phones, 264, 270 Loyalty schemes, 419 MAC (media access control) layer functions of, 91 scalable content delivery, 91 92 WCDMA, 14 Macrocell environment, 31 Mail servers, 258 Mail service, 147, 165 Manual mesh, distribution tree, 113 Mapping, data synchronization, 263, 270, 273 Matching, location estimation, 451 453, 455 Maximum transmission unit (MTU), 281, 353 MBone, 89 Media discovery IPDC system, 356 IP multicast system, 338, 340 341 Media gateway (MGW), 13 Media gateway control function (MGCF), 13 Mediation, charging issues, 428 Media transcoder, architecture of, 218, 221 222

Motwani, Clustering Data Streams: Theory and Practice IEEE Trans on Knowledge and Data Engineering, 15, 2003, 515 528 9 Y P Huang, C C Hsu, S H Wang, Pattern Recognition in Time Series Database: A Case Study, on Financial Database, Expert Systems with Applications, 33, 2007, 199 205 10 A K Jain, R C Dubes, Algorithms for Clustering Data Englewood Cliffs, NJ: Prentice-Hall, 1988 11 A Jain, M Murt, P Flynn, Data Clustering: A Review, ACM Computing Surveys, 31, 1999, 264 323 12 S Mitra, Y Hayashi, Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework IEEE Trans on Neural Networks, 11, 3, 2000, 748 768 13 C Molina, L Rodr guez-Ariza, D S nchez, M Amparo Vila, A New Fuzzy Multidimena sional Model IEEE Trans on Fuzzy Systems, 14, 6, 2006, 897 912 14 N H Park, W S.

The relative entropy D arises as the exponent in the probability of error in a hypothesis test between two distributions It is a natural measure of distance between distributions Statistical mechanics The entropy H arises in statistical mechanics as a measure of uncertainty or disorganization in a physical system Roughly speaking, the entropy is the logarithm of the number of ways in which the physical system can be con gured The second law of thermodynamics says that the entropy of a closed system cannot decrease Later we provide some interpretations of the second law Quantum mechanics Here, von Neumann entropy S = tr( ln ) = i i log i plays the role of classical Shannon Boltzmann entropy H = i pi log pi Quantum mechanical versions of data compression and channel capacity can then be found Inference.

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