Download Advances in Knowledge Discovery and Management: Volume 6 by Fabrice Guillet, Bruno Pinaud, Gilles Venturini PDF

By Fabrice Guillet, Bruno Pinaud, Gilles Venturini

ISBN-10: 3319457624

ISBN-13: 9783319457628

ISBN-10: 3319457632

ISBN-13: 9783319457635

This publication offers a set of consultant and novel paintings within the box of information mining, wisdom discovery, clustering and type, in response to increased and transformed types of a range of the simplest 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 e-book is in 3 components: the 1st 4 chapters speak about optimization issues in info 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 points of information discovery and management.

Show description

Read Online or Download Advances in Knowledge Discovery and Management: Volume 6 PDF

Similar data mining books

Advanced Methods for Knowledge Discovery from Complex Data

This publication brings jointly examine articles by way of lively practitioners and prime researchers reporting contemporary advances within the box of data discovery. an outline of the sector, taking a look at the problems and demanding situations concerned is through assurance of modern traits in facts mining. this offers the context for the next chapters on tools and purposes.

Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice

The phenomenon of volunteered geographic info is a part of a profound transformation in how geographic facts, details, and information are produced and circulated. via situating volunteered geographic details (VGI) within the context of big-data deluge and the data-intensive inquiry, the 20 chapters during this booklet discover either the theories and purposes of crowdsourcing for geographic wisdom construction with 3 sections targeting 1).

Big data Related Technologies, Challenges and Future Prospects

This Springer short offers a accomplished review of the history and up to date advancements of massive facts. the price chain of massive information is split into 4 levels: information iteration, facts acquisition, info garage and knowledge research. for every section, the booklet introduces the final historical past, discusses technical demanding situations and reports the most recent advances.

Extra resources for Advances in Knowledge Discovery and Management: Volume 6

Sample text

The user may select points that are not necessarily optimal but that represent good alternatives to the regular skyline answers (in the case, for instance, where the latter look “too good to be true”). Finally, an element of the graded skyline is associated with two scores: a degree of membership to the skyline, and a typicality degree (that expresses the extent to which it is not exceptional). One may imagine different ways of navigating inside areas in order to explore the set of answers: a simple scan for displaying the characteristics of the points, the use of different filters aimed, for instance, at optimizing diversity on certain attributes, etc.

Given a set of points in a space, a skyline query retrieves those points that are not dominated by any other in the sense of Pareto order. When the number of dimensions on which preferences are expressed gets high, many tuples may become incomparable. Several approaches have been proposed to define an order for two incomparable tuples, based on the number of other tuples that each of the two tuples dominates (notion of k-representative dominance proposed in Lin et al. (2007)), on a preference order over the attributes (see for instance the notions of k-dominance and k-frequency introduced in Chan et al.

Sahami, M. (1996). Toward optimal feature selection. In International Conference on Machine Learning (pp. 284–292). Kuncheva, L. , & Rodríguez, J. J. (2007). Classifier ensembles with a random linear oracle. IEEE Transactions on Knowledge and Data Engineering, 19(4), 500–508. Lange, K. (2004). Optimization. Springer Texts in Statistics. New York: Springer. , & Thompson, K. (1992). An analysis of bayesian classifiers. In National Conference on Artificial Intelligence (pp. 223–228). Nesterov, Y.

Download PDF sample

Advances in Knowledge Discovery and Management: Volume 6 by Fabrice Guillet, Bruno Pinaud, Gilles Venturini

by James

Rated 4.27 of 5 – based on 10 votes