Descriptor
Cluster Grouping | 4 |
Mathematical Formulas | 4 |
Tables (Data) | 4 |
Information Retrieval | 3 |
Search Strategies | 3 |
Probability | 2 |
Relevance (Information… | 2 |
Subject Index Terms | 2 |
Algorithms | 1 |
Comparative Analysis | 1 |
Correlation | 1 |
More ▼ |
Publication Type
Journal Articles | 4 |
Reports - Research | 4 |
Opinion Papers | 1 |
Education Level
Audience
Researchers | 1 |
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating

Thom, James A.; Zobel, Justin – Journal of the American Society for Information Science, 1992
Discusses models for the distribution of words in text and proposes a new model based on clustering that can be used to estimate the probability that a document contains a particular word as well as the number of distinct words in a document. Zipf's law and the Poisson approximation are also discussed. (18 references) (LRW)
Descriptors: Cluster Grouping, Mathematical Formulas, Models, Probability

van Rijsbergen, C. J.; And Others – Information Processing and Management, 1981
Describes the use of relevance feedback to select additional search terms and discusses the extraction of these terms from a maximum spanning tree connecting all terms in the index term vocabulary; retrieval effectiveness for different spanning trees is shown to be similar. Eight references are included. (Author/BK)
Descriptors: Cluster Grouping, Feedback, Information Retrieval, Mathematical Formulas

Can, Fazli – Information Processing and Management, 1994
Discussion of relevancy in information retrieval systems focuses on an analysis of the efficiency of various cluster-based retrieval (CBR) strategies. A method for combining CBR and inverted index search is proposed that is cost effective in terms of time efficiency; and results of experiments are reported. (Contains 32 references.) (LRW)
Descriptors: Algorithms, Cluster Grouping, Comparative Analysis, Cost Effectiveness

Radecki, Tadeusz – Information Processing and Management, 1985
Reports research results into a methodology for determining similarity between queries characterized by Boolean search request formulations and discusses similarity measures for Boolean combinations of index terms. Rationale behind these measures is outlined, and conditions ensuring their equivalence are identified. Results of an experiment…
Descriptors: Cluster Grouping, Correlation, Indexing, Information Retrieval