Classification as a Tool of Research


Book Description

This work contains a selection of papers presented at the meeting. The subjects covered include: Data analysis: Methods of scaling, Loglinear models, Correspondence analysis, Pattern recognition and discrimination, Analysis and aggregation of discrete structures, Measures of similarity and association. Numerical classification: Clustering methods, Robustness of methods, Fuzzy clustering, Statistical models. Concept analysis: Construction and reconstruction of concepts, Theories of characteristics and of definitions, Impact on artificial intelligence. Indexing languages and terminologies as information resources: Classification systems, Thesauri, Conceptual structure utilization, Identification of analogies. Software tools (especially on microcomputers): Availability of programs, Interfaces to data base systems, Information retrieval systems, Method base systems, Graphical representation, Comparisons of algorithms. Applications of classification examined here include economics, business administration, natural sciences, social science and humanities, chemistry research, library science, and linguistics. Contributors: P. Arabie, I. Balderjahn, P.M. Bentler, H.-H. Bock, I.




Classification as a Tool for Research


Book Description

Clustering and Classification, Data Analysis, Data Handling and Business Intelligence are research areas at the intersection of statistics, mathematics, computer science and artificial intelligence. They cover general methods and techniques that can be applied to a vast set of applications such as in business and economics, marketing and finance, engineering, linguistics, archaeology, musicology, biology and medical science. This volume contains the revised versions of selected papers presented during the 11th Biennial IFCS Conference and 33rd Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was organized in cooperation with the International Federation of Classification Societies (IFCS), and was hosted by Dresden University of Technology, Germany, in March 2009.




Classification as a Tool for Research


Book Description

Clustering and Classification, Data Analysis, Data Handling and Business Intelligence are research areas at the intersection of statistics, mathematics, computer science and artificial intelligence. They cover general methods and techniques that can be applied to a vast set of applications such as in business and economics, marketing and finance, engineering, linguistics, archaeology, musicology, biology and medical science. This volume contains the revised versions of selected papers presented during the 11th Biennial IFCS Conference and 33rd Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was organized in cooperation with the International Federation of Classification Societies (IFCS), and was hosted by Dresden University of Technology, Germany, in March 2009.







The Classification of Research


Book Description

Based on a selection of ten papers from the 1984 SRA Research Symposium in San Diego.




Using Classification and Regression Trees


Book Description

Classification and regression trees (CART) is one of the several contemporary statistical techniques with good promise for research in many academic fields. There are very few books on CART, especially on applied CART. This book, as a good practical primer with a focus on applications, introduces the relatively new statistical technique of CART as a powerful analytical tool. The easy-to-understand (non-technical) language and illustrative graphs (tables) as well as the use of the popular statistical software program (SPSS) appeal to readers without strong statistical background. This book helps readers understand the foundation, the operation, and the interpretation of CART analysis, thus becoming knowledgeable consumers and skillful users of CART. The chapter on advanced CART procedures not yet well-discussed in the literature allows readers to effectively seek further empowerment of their research designs by extending the analytical power of CART to a whole new level. This highly practical book is specifically written for academic researchers, data analysts, and graduate students in many disciplines such as economics, social sciences, medical sciences, and sport sciences who do not have strong statistical background but still strive to take full advantage of CART as a powerful analytical tool for research in their fields.




Theory and Practice of Business Intelligence in Healthcare


Book Description

Business intelligence supports managers in enterprises to make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data (big data) in the healthcare industry. Because of the complex nature of healthcare data and the significant impact of healthcare data analysis, it is important to understand both the theories and practices of business intelligence in healthcare. Theory and Practice of Business Intelligence in Healthcare is a collection of innovative research that introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big volumes of data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. While highlighting topics including digital health, operations intelligence, and patient empowerment, this book is ideally designed for healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers seeking current research on the digitization of health records and health systems integration.




Developing and Testing a Tool for the Classification of Study Designs in Systematic Reviews of Interventions and Exposures


Book Description

BACKGROUND: Classification of study design can help provide a common language for researchers. Within a systematic review, definition of specific study designs can help guide inclusion, assess the risk of bias, pool studies, interpret results, and grade the body of evidence. However, recent research demonstrated poor reliability for an existing classification scheme. OBJECTIVES: To review tools used to classify study designs; to select a tool for evaluation; to develop instructions for application of the tool to intervention/exposure studies; and to test the tool for accuracy and interrater reliability. METHODS: We contacted representatives from all AHRQ Evidence-based Practice Centers (EPCs), other relevant organizations, and experts in the field to identify tools used to classify study designs. Twenty-three tools were identified; 10 were relevant to our objectives. The Steering Committee ranked the 10 tools using predefined criteria. The highest-ranked tool was a design algorithm for studies of health care interventions developed, but no longer advocated, by the Cochrane Non-Randomised Studies Methods Group. This tool was used as the basis for our classification tool and was revised to encompass more study designs and to incorporate elements of other tools. A sample of 30 studies was used to test the tool. Three members of the Steering Committee developed a reference standard (i.e., the "true" classification for each study); 6 testers applied the revised tool to the studies. Interrater reliability was measured using Fleiss' kappa (o) and accuracy of the testers' classification was assessed against the reference standard. Based on feedback from the testers and the reference standard committee, the tool was further revised and tested by another 6 testers using 15 studies randomly selected from the original sample. RESULTS: In the first round of testing the inter-rater reliability was fair among the testers (o = 0.26) and the reference standard committee (o = 0.33). Disagreements occurred at all decision points in the algorithm; revisions were made based on the feedback. The second round of testing showed improved interrater reliability (o = 0.45, moderate agreement) with improved, but still low, accuracy. The most common disagreements were whether the study was "experimental" (5/15 studies) and whether there was a comparison (4/15 studies). In both rounds of testing, the level of agreement for testers who had completed graduate-level training was higher than for testers who had not completed training. CONCLUSION: Potential reasons for the observed low reliability and accuracy include the lack of clarity and comprehensiveness of the tool, inadequate reporting of the studies, and variability in user characteristics. Application of a tool to classify study designs in the context of a systematic review should be accompanied by adequate training, pilot testing, and documented decision rules.




Data Classification


Book Description

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi