000			03726nam a22003974i 4500
005			20250918233759.0
008			140402s2014 flua b 001 0 eng
020			_a9781439860847 (hbk.) _cRM255.79
020			_a143986084X (hardback)
039		9	_a201405201239 _brosli _c201405201227 _drosli _c201405141255 _dhamudah _y04-02-2014 _zhamudah
040			_aDLC _beng _erda _cDLC _dOCLCO _dCGU _dYDXCP _dIQU _dOKU _dIG# _dUKM
090			_aQA76.9.D343P733
090			_aQA76.9.D343 _bP733
245	0	0	_aPractical graph mining with R / _ceditors, Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty.
264		1	_aBoca Raton : _bTaylor & Francis, _c2014.
300			_axxi, 473 pages : _billustrations ; _c25 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	1		_aChapman & Hall/CRC data mining and knowledge discovery series
504			_aIncludes bibliographical references and index.
520			_a'Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a'do-it-yourself' approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs.Hands-On Application of Graph Data MiningEach chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks.Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical FoundationsEvery algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique.Makes Graph Mining Accessible to Various Levels of ExpertiseAssuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners'-- _cProvided by publisher.
650		0	_aData mining _xGraphic methods.
650		0	_aData visualization _xData processing.
650		0	_aR (Computer program language).
700	1		_aSamatova, Nagiza F.
856	4	2	_3Cover image _uhttp://images.tandf.co.uk/common/jackets/websmall/978143986/9781439860847.jpg
907			_a.b15863475 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kQA76.9.D343P733
914			_avtls003555280
990			_ark4
991			_aFakulti Sains dan Teknologi Maklumat
998			_at _b2014-02-04 _cm _da _feng _gflu _y0 _z.b15863475
999			_c566178 _d566178