TAILIEUCHUNG - Managing and Mining Graph Data part 6

Managing and Mining Graph Data part 6 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. . | Graph Data Management and Mining A Survey of Algorithms and Applications 3 1 model-based search tree or MbT 7l . The idea is to use divide and conquer to mine ibc moit siteisifir-ant patterns in a subspace of examples. It builds a decision tree part-tions -he eiaia onto dtfferent nodes. Then at each node it directly discovers v discthmnattvc o -ttern lo fuslhcr divide its examples into purer subseSs. l nets the number of examples towards leaf level is relatively smalt. this approach is allo ti examins patterns with extremely low global support that core lei not he enumerated on the whole data set. For some graph data sets which occue io drug discovery apphcations 71 it could mine significant graph patterns which is very dinicuii. for most other solutions. Since it uses tire dis-ide and conquer ptradigm. iStc afttiieilhm is almost linearly scalable with 1 MinSupport and tits number of ex an p est71 The MbT technique is xoí. iímited to etaehs. bub atso applicable item sets and sequences and mine pattern se t ir both small and significant. Ono of the key challenger which arises in the context of all frequent pat-ttssn -nining algorithms is the massive numhas of patterns which can be mined from the undetiying database. This proNcm Is particularly acute in the case of gr iipf it since site of the ou-puL cun be extremely large. One solution for seducing the oumbei of rcpsercntalivc patterm is So report frequent patterns in tesms of orthogonality. A model called ORIGAMI lias tses n proposed in 93 which teposts frequent graph aattcsns oniy if the similarity is below a threshold a. Such patterns art also sefereed to as a-orthogonal patterns. A pattern set P ís iaid bt be -representative if fot every non-reported pattern q at t tsS one pattern can be found in P for whtch the undeiSying similarity to q ís tl. Irasl a thseshold fi. Thete two conniraints addicts xOflcran- aspects of the structural paitcres. The method tn 193í slelermincs the set of all .

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