Generating association rules from semi-structured documents using an extended concept hierarchy

1997 ◽  
Author(s):  
Lisa Singh ◽  
Peter Scheuermann ◽  
Bin Chen
Keyword(s):  
Association Rules ◽  
Concept Hierarchy ◽  
Structured Documents

Discovering Personalized Novel Knowledge from Text

2010 ◽  
pp. 301-313
Author(s):  
Yi-fang Brook Wu ◽  
Xin Chen
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Noun Phrases ◽  
Background Knowledge ◽  
Semantic Distance ◽  
Concept Hierarchy ◽  
Better Than

This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules derived and many already known to the user. Our proposed algorithm derives user’s background knowledge from a set of documents provided by the user, and exploits such knowledge in the process of knowledge discovery from text. Keywords are extracted from background documents and clustered into a concept hierarchy that captures the semantic usage of keywords and their relationships in the background documents. Target documents are retrieved by selecting documents that are relevant to the user’s background. Association rules are discovered among noun phrases extracted from target documents. Novelty of an association rule is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. The experiment shows that our novelty measure performs better than support and confidence in identifying novel knowledge.

Mining association rules for concept hierarchy in large database

2018 ◽  
Vol 21 (2) ◽  
pp. 457-467
Author(s):  
Chien-Hua Wang ◽  
Wei-Hsuan Lee ◽  
Chin-Tzong Pang
Keyword(s):  
Association Rules ◽  
Large Database ◽  
Concept Hierarchy ◽  
Mining Association Rules

Marking Up is Not Enough

1993 ◽  
Vol 32 (04) ◽  
pp. 272-273 ◽  
Author(s):  
A. L. Rector
Keyword(s):  
Health Care ◽  
Intelligent Processing ◽  
Structured Documents ◽  
Electronic Health ◽  
Health Care Records

Response to: Essin DJ. Intelligent processing of loosely structured documents as a strategy for organizing electronic health care records. Meth Inform Med 1993; 32: 265.

Intelligent Processing of Loosely Structured Documents as a Strategy for Organizing Electronic Health Care Records

1993 ◽  
Vol 32 (04) ◽  
pp. 265-268 ◽  
Author(s):  
D. J. Essin
Keyword(s):  
Health Care ◽  
Health Care Organization ◽  
Database Systems ◽  
Relevant Information ◽  
Full Potential ◽  
Care Organization ◽  
Intelligent Processing ◽  
Data Interchange ◽  
Structured Documents ◽  
Health Care Records

AbstractLoosely structured documents can capture more relevant information about medical events than is possible using today’s popular databases. In order to realize the full potential of this increased information content, techniques will be required that go beyond the static mapping of stored data into a single, rigid data model. Through intelligent processing, loosely structured documents can become a rich source of detailed data about actual events that can support the wide variety of applications needed to run a health-care organization, document medical care or conduct research. Abstraction and indirection are the means by which dynamic data models and intelligent processing are introduced into database systems. A system designed around loosely structured documents can evolve gracefully while preserving the integrity of the stored data. The ability to identify and locate the information contained within documents offers new opportunities to exchange data that can replace more rigid standards of data interchange.

How Useful Can Be Data Mining For A Continuos Speech Therapist’s Education?

2014 ◽  
Vol 1 (1) ◽  
pp. 339-342
Author(s):  
Mirela Danubianu ◽  
Dragos Mircea Danubianu
Keyword(s):  
Data Mining ◽  
Information And Communication Technology ◽  
Association Rules ◽  
Communication Technology ◽  
Speech Therapy ◽  
Proper Treatment ◽  
Speech Impairments ◽  
Information And Communication ◽  
Specific Education

AbstractSpeech therapy can be viewed as a business in logopaedic area that aims to offer services for correcting language. A proper treatment of speech impairments ensures improved efficiency of therapy, so, in order to do that, a therapist must continuously learn how to adjust its therapy methods to patient's characteristics. Using Information and Communication Technology in this area allowed collecting a lot of data regarding various aspects of treatment. These data can be used for a data mining process in order to find useful and usable patterns and models which help therapists to improve its specific education. Clustering, classification or association rules can provide unexpected information which help to complete therapist's knowledge and to adapt the therapy to patient's needs.

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