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Saarela, Mirka; Kärkkäinen, Tommi – International Educational Data Mining Society, 2015
Certain stereotypes can be associated with people from different countries. For example, the Italians are expected to be emotional, the Germans functional, and the Chinese hard-working. In this study, we cluster all 15-year-old students representing the 68 different nations and territories that participated in the latest Programme for…
Descriptors: Weighted Scores, Stereotypes, Standardized Tests, Student Characteristics
Zhang, Yi – ProQuest LLC, 2011
Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be…
Descriptors: Information Technology, Data Processing, Data Analysis, Information Retrieval
Raths, David – Campus Technology, 2010
In the tug-of-war between researchers and IT for supercomputing resources, a centralized approach can help both sides get more bang for their buck. As 2010 began, the University of Washington was preparing to launch its first shared high-performance computing cluster, a 1,500-node system called Hyak, dedicated to research activities. Like other…
Descriptors: Educational Finance, Researchers, Information Technology, Competition
Madhyastha, Tara; Hunt, Earl – Journal of Educational Data Mining, 2009
This paper introduces a method for mining multiple-choice assessment data for similarity of the concepts represented by the multiple choice responses. The resulting similarity matrix can be used to visualize the distance between concepts in a lower-dimensional space. This gives an instructor a visualization of the relative difficulty of concepts…
Descriptors: Diagnostic Tests, Multiple Choice Tests, Concept Formation, Schematic Studies

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
Brown, R. L. – 1984
The plotting of multivariate data using computer line-printers has become a popular means of quickly representing multidimensional data. While many plotting programs are available, there is a paucity of research regarding the validity and reliability of interpretations made by viewing such graphics. This study explores the validity of four…
Descriptors: Cluster Grouping, Computer Graphics, Computer Simulation, Data Analysis

Ruocco, Anthony S.; Frieder, Ophir – Journal of the American Society for Information Science, 1997
Proposes use of parallel computing systems to overcome the computationally intense clustering process. Results show some near linear speed up in higher threshold clustering applications, meeting the requirements to classify, group and process large document sets within nonprohibitive execution times. Includes graphs and charts. (JAK)
Descriptors: Access to Information, Classification, Cluster Analysis, Cluster Grouping
McKinlay, Donald Bruce – 1971
The need for more and better manpower information is hampered by the lack of adequate occupational data classification systems. The diversity of interests in occupations probably accounts for the absence of consensus regarding either the general outlines or the specific details of a standardized occupational classification system which would…
Descriptors: Classification, Cluster Grouping, Data Collection, Data Processing

Larson, Ray R. – Journal of the American Society for Information Science, 1992
Presents the results of research into the automatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records from a test database at the University of California at Berkeley Library School library. Classification clustering and matching techniques are described. (44 references) (LRW)
Descriptors: Academic Libraries, Bibliographic Databases, Bibliographic Records, Classification
Becker, David S.; Pyrce, Sharon R. – 1977
The goal of this project was to find ways of enhancing the efficiency of searching machine readable data bases. Ways are sought to transfer to the computer some of the tasks that are normally performed by the user, i.e., to further automate information retrieval. Four experiments were conducted to test the feasibility of a sequential processing…
Descriptors: Algorithms, Bibliographic Coupling, Cluster Grouping, Computers