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Soh, Kay Cheng – Higher Education Review, 2012
Three university ranking systems in vogue have been shown in the previous issue of "Higher Education Review" to be capable of modifications to make them more parsimonious by using only about half of the number of predictors currently in use. This makes some of the predictors "redundant" as they contributed little to the overall ranking. It is…
Descriptors: Higher Education, Predictor Variables, Profiles, Test Items
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Kohn, Hans-Friedrich; Steinley, Douglas; Brusco, Michael J. – Psychological Methods, 2010
The "p"-median clustering model represents a combinatorial approach to partition data sets into disjoint, nonhierarchical groups. Object classes are constructed around "exemplars", that is, manifest objects in the data set, with the remaining instances assigned to their closest cluster centers. Effective, state-of-the-art implementations of…
Descriptors: Computer Software, Psychological Studies, Data Analysis, Research Methodology
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Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment
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Frants, Valery I.; And Others – Information Processing and Management, 1993
Shows how to automatically construct a classification of users and a clustering of documents and cross-references among clusters based on users' information needs. Feedback in the construction of this classification and clustering that allows for the classification to be changed to reflect changing needs of users is also described. (22 references)…
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Expert Systems
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Boley, Daniel; Gini, Maria; Hastings, Kyle; Mobasher, Bamshad; Moore, Jerry – Internet Research, 1998
Describes WebACE, the architecture of a client-side agent that explores and classifies Web documents in clusters automatically and discusses the details of the algorithms within its key components. Highlights principal direction divisive partitioning (PDDP), a scalable hierarchical clustering algorithm; compares it to other clustering methods; and…
Descriptors: Algorithms, Automation, Classification, Cluster Grouping
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Bureau of Labor Statistics (DOL), Washington, DC. – 1999
In 1994, a committee was formed to revise the Standard Occupational Classification (SOC) system to meet the needs of the 21st century. The committee, which was chartered by the Bureau of Labor Statistics and the Bureau of the Census, included representatives from eight public agencies using occupational information. The SOC revision process…
Descriptors: Agency Cooperation, Agency Role, Classification, Cluster Grouping