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DATA MINING:
A TOOL FOR KNOWLEDGE
    MANAGEMENT

Prepared by:
          Bhagawati Narzari
          Dhiru Barman
          Ridip Jyoti Kalita
What We Will Cover Today:

   Introducing Data Mining
   Scope of Data Mining
   Classes of Data Mining
   Elements of Data Mining
   Data Mining and Knowledge Management
   Data Mining in Libraries
   Bibliomining
   Conclusion
                  SIS-2012                 2
Introducing Data Mining
   Data mining is one process of extracting patterns from
    data. Data mining involves sorting through large
    amounts of data and picking out relevant information.
    Data mining can be used in any organization including
    library to apply to the two separate processes of
    knowledge discovery and prediction. Data mining is one
    of the important parts of Bibliomining, where large
    amount of data are associated with the library systems in
    order to aid decision-making or justify services. Data
    mining and its elements, functions, process and some
    other involving factors have been discussed in this
    paper.
                         SIS-2012                           3
Scope of Data Mining
   Automated prediction of trends and behaviors:
    Data mining automates the process of finding predictive information
    in large databases. Questions that traditionally required extensive
    hands-on analysis can now be answered directly from the data —
    quickly.
   Automated        discovery        of    previously       unknown
    patterns:
    Data mining tools sweep through databases and identify previously
    hidden patterns in one step. An example of pattern discovery is the
    analysis of retail sales data to identify seemingly unrelated products
    that are often purchased together.



                              SIS-2012                                   4
Traditional Data Mining Process




             SIS-2012             5
Classes of Data Mining

   Predicting
   Classification
   Detection of relations
   Explicit modeling
   Clustering
   Market Basket Analysis
   Deviation Detection


                  SIS-2012         6
Elements of Data Mining


   Extract, transform, and load transaction data onto
    the data warehouse system
   Store and manage the data in a multidimensional
    database system
   Provide data access to business analysts and
    information technology professionals.
   Analyze the data by application software.
   Present the data in a useful format, such as a graph
    or table.


                        SIS-2012                           7
Possible Questions on Data Mining in LISc
Data       Possible Question            Enabling   Section           Service
Ming in                                 Technolo   Belonging         Belonging
Library                                 gies
SL. NO.1   “How many books              Computer, Acquisition        Lending
           acquired last year           Library   Section            service,
           regarding science            software                     Document
           stream”                                                   delivery
                                                                     service
SL. NO.2   “How many                    Computer, Reference          Reference
           encyclopedias are there      Library   Section            and
           at present in the library”   software                     Information
                                                                     Service
SL. NO.3   “How many subscribed         Computer, Periodical Section Periodical
           science journals are         Library                      Service
           there at present in the      software
           library”
SL. NO.4   “Which are the               Computer, Bound Periodical   Periodical
           newspaper that has           Library   Section/Back       Service
           been kept in bound           software  Volume Section
Bibliomining

A new term to describe the data mining process in
libraries is Bibliomining (Nicholson and Stanton, In
press). Bibliomining is defined as “the combination of
data mining, bibliometrics, statistics, and reporting tools
used to extract patterns of behavior-based artifacts from
library systems” (Nicholson, 2002). Instead of behavior-
based artifacts, however, this project is using
bibliomining to discover patterns in artifacts contained in
and associated with Web pages. The techniques to
discover novel and actionable patterns still apply.




                      SIS-2012                                9
Conclusion
   The need and application of data mining has
    become essential to manage, organize, and
    disseminate information to the right users at right
    time. Though it is primarily intended for the business
    class, still then it has got practical implications in
    Libraries and Information Centers due to
    overwhelming growth of literature especially in
    digital formats. Now-a-days, more and more digital
    data are being collected, processed, managed and
    archived in Libraries and Information Centers to suit
    to the varied need of the user communities every
    day.
THANK YOU

   SIS-2012   11

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Data mining a tool for knowledge management

  • 1. DATA MINING: A TOOL FOR KNOWLEDGE MANAGEMENT Prepared by: Bhagawati Narzari Dhiru Barman Ridip Jyoti Kalita
  • 2. What We Will Cover Today:  Introducing Data Mining  Scope of Data Mining  Classes of Data Mining  Elements of Data Mining  Data Mining and Knowledge Management  Data Mining in Libraries  Bibliomining  Conclusion SIS-2012 2
  • 3. Introducing Data Mining  Data mining is one process of extracting patterns from data. Data mining involves sorting through large amounts of data and picking out relevant information. Data mining can be used in any organization including library to apply to the two separate processes of knowledge discovery and prediction. Data mining is one of the important parts of Bibliomining, where large amount of data are associated with the library systems in order to aid decision-making or justify services. Data mining and its elements, functions, process and some other involving factors have been discussed in this paper. SIS-2012 3
  • 4. Scope of Data Mining  Automated prediction of trends and behaviors: Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data — quickly.  Automated discovery of previously unknown patterns: Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. SIS-2012 4
  • 5. Traditional Data Mining Process SIS-2012 5
  • 6. Classes of Data Mining  Predicting  Classification  Detection of relations  Explicit modeling  Clustering  Market Basket Analysis  Deviation Detection SIS-2012 6
  • 7. Elements of Data Mining  Extract, transform, and load transaction data onto the data warehouse system  Store and manage the data in a multidimensional database system  Provide data access to business analysts and information technology professionals.  Analyze the data by application software.  Present the data in a useful format, such as a graph or table. SIS-2012 7
  • 8. Possible Questions on Data Mining in LISc Data Possible Question Enabling Section Service Ming in Technolo Belonging Belonging Library gies SL. NO.1 “How many books Computer, Acquisition Lending acquired last year Library Section service, regarding science software Document stream” delivery service SL. NO.2 “How many Computer, Reference Reference encyclopedias are there Library Section and at present in the library” software Information Service SL. NO.3 “How many subscribed Computer, Periodical Section Periodical science journals are Library Service there at present in the software library” SL. NO.4 “Which are the Computer, Bound Periodical Periodical newspaper that has Library Section/Back Service been kept in bound software Volume Section
  • 9. Bibliomining A new term to describe the data mining process in libraries is Bibliomining (Nicholson and Stanton, In press). Bibliomining is defined as “the combination of data mining, bibliometrics, statistics, and reporting tools used to extract patterns of behavior-based artifacts from library systems” (Nicholson, 2002). Instead of behavior- based artifacts, however, this project is using bibliomining to discover patterns in artifacts contained in and associated with Web pages. The techniques to discover novel and actionable patterns still apply. SIS-2012 9
  • 10. Conclusion  The need and application of data mining has become essential to manage, organize, and disseminate information to the right users at right time. Though it is primarily intended for the business class, still then it has got practical implications in Libraries and Information Centers due to overwhelming growth of literature especially in digital formats. Now-a-days, more and more digital data are being collected, processed, managed and archived in Libraries and Information Centers to suit to the varied need of the user communities every day.
  • 11. THANK YOU SIS-2012 11