based in modern industrial societies. Some post-modern theorists have gone even further by questioning whether class still exists. Jan Pakulski and Malcolm Water (1996) argue that classes exist only if there is ’minimum level of clustering ‚ or groupness ’ and such clusterings or groupness are no longer evident. People no longer feel that they belong to class groupings‚ and members of
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Gentrification the process of neighborhood change that results in the replacement of lower income residents with higher income ones. Revitalization • The process of enhancing the physical‚ commercial and social components of neighborhoods and the future prospects of its residents through private sector and/or public sector efforts. Physical components include upgrading of housing stock and streetscapes. • Commercial components include the creation of viable businesses and services in the community
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(“OHADA”) was a consequence of the awareness by certain African states - mostly former French colonies - of the challenge represented by the globalization of markets. While in Latin America it comes to formulating cluster-oriented policies. Clustering seems to enable
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Title: Mining High Dimensional Educational (Student) Data to Extract Patterns for Interestingness An Introduction to Data Mining Overview Data mining‚ the extraction of hidden predictive information from large databases‚ is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors‚ allowing businesses to make proactive‚ knowledge-driven decisions. Data mining tools can
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A BigBench Implementation on the Hadoop Ecosystem Badrul Chowdhury‚ Tilmann Rabl‚ and Hans-Arno Jacobsen Middleware Systems Research Group University of Toronto badrul.chowdhury@mail.utoronto.ca‚ tilmann.rabl@utoronto.ca‚ jacobsen@eecg.toronto.edu http://msrg.org Abstract. BigBench is the first proposal for an end to end big data analytics benchmark. It features a rich query set with complex‚ realistic queries. BigBench was developed based on the decision support benchmark TPC-DI. The
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How to Read Faster: Bill Cosby’s Three Proven Strategies by Maria Popova “Nobody gets something for nothing in the reading game.” “All attempts at gaining literary polish must begin with judicious reading‚” H. P. Lovecraft famously advised aspiring writers. Indeed‚ reading is an essential skill on par with writing‚ and though non-reading may be an intellectual choice on par with reading‚ reading itself — just like writing — is a craft that requires optimal technique for optimal outcome. So how
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Visual Categorization with Bags of Keypoints Gabriella Csurka‚ Christopher R. Dance‚ Lixin Fan‚ Jutta Willamowski‚ Cédric Bray Xerox Research Centre Europe 6‚ chemin de Maupertuis 38240 Meylan‚ France {gcsurka‚cdance}@xrce.xerox.com Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization
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Edelmann-Nusser‚ Hohmann‚ & Henneberg‚ 2002; Ofoghi‚ Zeleznikow‚ MacMahon‚ & Dwyer‚ 2010). These can be used in the decision-making processes to support strategic planning and athlete selection. Commonly Used Data Mining Methods in Elite Sports Clustering. Clustering is one form of unsupervised learning that is concerned with finding how the data are organized and summarizing/explaining key features of the data (Clausen‚ 2012). The result of a cluster analysis is the formation of a number of groups. The
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Chicago’s Tribunes Server Consolidation a Success Summary This case study is an analysis of the Chicago Tribunes Server consolidation in which the Chicago Tribune moved its critical applications from several mainframes and older Sun servers to new‚ dual-site data-center infrastructure based on Sun 15K servers. The Tribune clustered the Sun servers over a 2-mile distance‚ lighting up a dark-fiber‚ 1-Gbps link between two data centers. This configuration let the newspaper spread the processing
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Unsupervised Learning Goal: Segment data into meaningful segments; detect patterns There is no target (outcome) variable to predict or classify – no need to partition data Methods: Association rules‚ data reduction & exploration‚ visualization‚ clustering Supervised Classification: Goal: Predict categorical target (outcome) variable Examples: Purchase/no purchase‚ fraud/no fraud‚ creditworthy/not creditworthy… Target variable is often binary (yes/no) Prediction Goal: Predict numerical target
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