Tm

The educational technology and digital learning wiki
Revision as of 13:23, 25 September 2014 by Daniel K. Schneider (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search
Rlogo.jpg


Tm - Text mining package 0.5-10 (2014/01/13)

Tm.png

Developed by: Ingo Feinerer
License: GPL / GNU General Public License
Web page : Tool homepage
Tool type : Plugin of R

Tool.png

The last edition of this page was on: 2014/03/24

The Completion level of this page is : Medium


SHORT DESCRIPTION

tm package provides a framework for text mining applications within R. The tm package offers functionality for managing text documents, abstracts the process of document manipulation and eases the usage of heterogeneous text formats in R. The package provides native support for reading in several classic file formats such as plain text, PDFs, or XML files. There is also a plug-in mechanism to handle additional file formats. The data structures and algorithms can be extended to fit custom demands.


TOOL CHARACTERISTICS

Usability

Authors of this page consider that this tool is '.

Tool orientation

This tool is designed for general purpose analysis.

Data mining type

This tool is made for Structured data mining, Text mining.

Manipulation type

This tool is designed for Data extraction, Data transformation, Data analysis, Data visualisation.

IMPORT FORMAT : PDF, TXT, XML

EXPORT FORMAT :


Tool objective(s) in the field of Learning Sciences

Analysis & Visualisation of data
Predicting student performance
Student modelling
Social Network Analysis (SNA)
Constructing courseware

Providing feedback for supporting instructors:
Recommendations for students
Grouping students:
Developing concept maps:
Planning/scheduling/monitoring
Experimentation/observation

Tool can perform:

  • Data extraction of type:
  • Transformation of type:
  • Data analysis of type: Basic statistics and data summarization, Data mining methods and algorithms
  • Data visualisation of type: Sequential Graphic, Chart/Diagram, Map (These visualisations can be interactive and updated in "real time")



ABOUT USERS

Tool is suitable for:

Students/Learners/Consumers
Teachers/Tutors/Managers
Researchers
Developers/Designers
Organisations/Institutions/Firms
Others

Required skills:

STATISTICS: Advanced

PROGRAMMING: Basic

SYSTEM ADMINISTRATION: None

DATA MINING MODELS: Advanced



FREE TEXT


Tool version : Tm - Text mining package 0.5-10 2014/01/13
(blank line)

Developed by : Ingo Feinerer
(blank line)
Tool Web page : http://tm.r-forge.r-project.org/
(blank line)
Tool type : Plugin of R
(blank line)
License:GPL / GNU General Public License

Tm.png

SHORT DESCRIPTION


tm package provides a framework for text mining applications within R. The tm package offers functionality for managing text documents, abstracts the process of document manipulation and eases the usage of heterogeneous text formats in R. The package provides native support for reading in several classic file formats such as plain text, PDFs, or XML files. There is also a plug-in mechanism to handle additional file formats. The data structures and algorithms can be extended to fit custom demands.

TOOL CHARACTERISTICS


Tool orientation Data mining type Usability
This tool is designed for general purpose analysis. This tool is designed for Structured data mining, Text mining. Authors of this page consider that this tool is .
Data import format Data export format
PDF, TXT, XML. .
Tool objective(s) in the field of Learning Sciences

☑ Analysis & Visualisation of data
☑ Predicting student performance
☑ Student modelling
☑ Social Network Analysis (SNA)
☑ Constructing courseware

☑ Providing feedback for supporting instructors:
☑ Recommendations for students
☑ Grouping students:
☑ Developing concept maps:
☑ Planning/scheduling/monitoring
Experimentation/observation

Can perform data extraction of type:

Can perform data transformation of type:


Can perform data analysis of type:
Basic statistics and data summarization, Data mining methods and algorithms


Can perform data visualisation of type:
Sequential Graphic, Chart/Diagram, Map (These visualisations can be interactive and updated in "real time")


ABOUT USER


Tool is suitable for:
Students/Learners/Consumers:☑ Teachers/Tutors/Managers:☑ Researchers:☑ Organisations/Institutions/Firms:☑ Others:☑
Required skills:
Statistics: ADVANCED Programming: BASIC System administration: NONE Data mining models: ADVANCED

OTHER TOOL INFORMATION


Tm.png
Tm.png
Rlogo.jpg
Tm - Text mining package
GPL / GNU General Public License
Free&Open source
Ingo Feinerer
2014/01/13
0.5-10
http://tm.r-forge.r-project.org/
tm package provides a framework for text mining applications within R. The tm package offers functionality for managing text documents, abstracts the process of document manipulation and eases the usage of heterogeneous text formats in R. The package provides native support for reading in several classic file formats such as plain text, PDFs, or XML files. There is also a plug-in mechanism to handle additional file formats. The data structures and algorithms can be extended to fit custom demands.
General analysis
Teachers/Tutors/Managers, Researchers
Advanced
Basic
None
Advanced
Plugin/extension pack
Structured data mining, Text mining
Data extraction, Data transformation, Data analysis, Data visualisation
Basic statistics and data summarization, Data mining methods and algorithms
PDF, TXT, XML
Sequential Graphic, Chart/Diagram, Map
R
Medium

Publications

  • Ingo Feinerer, Kurt Hornik, David Meyer (2008). Text Mining Infrastructure in R, Journal of Statistics Sofware, Vol. 25, Issue 5, Mar 2008. http://www.jstatsoft.org/v25/i05.
    • Abstract: During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

Documentation

  • Try the built-in documentation first, e.g. type ??tm