By Fabrice Guillet, Bruno Pinaud, Gilles Venturini
This e-book offers a suite of consultant and novel paintings within the box of knowledge mining, wisdom discovery, clustering and category, according to elevated and transformed models of a variety of the easiest papers initially offered in French on the EGC 2014 and EGC 2015 meetings held in Rennes (France) in January 2014 and Luxembourg in January 2015. The publication is in 3 components: the 1st 4 chapters talk about optimization concerns in facts mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters specialise in semantics, ontologies and social networks.
Written for PhD and MSc scholars, in addition to researchers operating within the box, it addresses either theoretical and sensible features of information discovery and management.
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Extra info for Advances in Knowledge Discovery and Management: Volume 6
2013; Zimek et al. 2013; Ji et al. 2013). The approaches may be categorized into three classes: (i) those that aim to isolate data distant from another normal data, (ii) those that aim to detect if a new observation is normal or abnormal, (iii) those that exclusively focus on modelling normality and assess if a new observation is likely to be normal. The estimation of the best approach for detecting outliers is out of the scope of this paper. In the tolerant skyline approach we propose, we use a simple detection technique based on the notions of frequency and distance.
1–15). Lecture notes in computer science. Berlin: Springer. Lee, K. C. -C. (2007). Approaching the skyline in z order. In VLDB (pp. 279–290). Lesot, M. (2006). Typicality-based clustering. International Journal of Information technology and Intelligent Computing, 12, 279–292. , & Zhang, Y. (2007). Selecting stars: The k most representative skyline operator. In Proceedings of the ICDE 2007 (pp. 86–95). 38 H. Jaudoin et al. , & He, X. (2011). A survey of outlier detection methodologies and their applications.
Fuzzy dominance skyline queries. In R. Wagner, N. Revell & G. ), Proceedings 18th International Conference on Database and Expert Systems Applications, DEXA 2007, Regensburg, Germany, 3–7 September 2007 (Vol. 4653, pp. 469–478). Lecture notes in computer science. Heidelberg: Springer. , and Han, J. (2013). Community distribution outlier detection in heterogeneous ˘ information networks. In H. Blockeel, K. Kersting, S. Nijssen & F. ), Machine learning and knowledge discovery in databases (Vol. 8188, pp.