By Fabrice Guillet, Bruno Pinaud, Gilles Venturini
This e-book offers a set of consultant and novel paintings within the box of knowledge mining, wisdom discovery, clustering and class, according to accelerated 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 ebook is in 3 components: the 1st 4 chapters speak about optimization issues in facts mining. the second one half explores particular caliber measures, dissimilarities and ultrametrics. the ultimate chapters specialize 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 points of data discovery and management.
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Additional info for Advances in Knowledge Discovery and Management: Volume 6
The closer the rate is to 1, the higher is the model likelihood. For model less competitive than the random model, compression rate is negative. The value of the compression rate on train data is then a good indicator of the optimization quality as the non regularized criterion is reduced to the negative log-likelihood. Figure 1 presents the train and test compression rate averaged on 36 UCI datasets for various mini-batches sizes L = 100, 1000, N . In the last case, the choice L = N corresponds to a batch algorithm.
2 relies on a gradual view of outliers where no threshold γ is applied. 000 24 H. Jaudoin et al. 3 Principle of the Exception-Tolerant Skyline As explained in the introduction, our goal is to revisit the definition of the skyline so as to take into account the typicality of the points in the database, in order to control the impact of exceptions or anomalies. Thus, three variants of the classical skyline are defined hereafter: 1 • SkyD that returns all sufficiently typical points of S that are not dominated by sufficiently typical points, 2 • SkyD that returns all points of S that are not dominated by sufficiently typical points, and 3 • SkyD that returns a fuzzy set of S , where each point is associated with a membership degree which is a function of the typicality of the points that dominate it.
In Y. ), COLING (pp. 312–318). ACL. Zadeh, L. (1987). A computational theory of dispositions. International Journal of Intelligent Systems, 2, 39–63. Zhang, J. (2013). Advancements of outlier detection: A survey. EAI Endorsed Transactions on Scalable Information Systems, 1, e2. , Campello, R. J. G. , & Sander, J. (2013). Subsampling for efficient and effective unsupervised outlier detection ensembles. In The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, 11–14 August 2013 (pp.