Data mining
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The document discusses data mining concepts, processes, and applications in libraries. It defines data mining as extracting patterns from large datasets using statistics and artificial intelligence. Key concepts discussed include data warehouses, metadata, and common metadata schemas. Processes of data mining outlined are creating databases, integrating data, formatting, organizing, naming data, searching/retrieving information. The need for data mining in libraries is due to huge quantities of information and the ability to satisfy user needs through better storage and retrieval systems.
Data mining
- 1. Devi Ahilya Vishva VidhyalyaDevi Ahilya Vishva Vidhyalya Taksha Shila Campus, Khandwa Road, IndoreTaksha Shila Campus, Khandwa Road, Indore DATA MININGDATA MINING (concepts, need and application in library)(concepts, need and application in library) A presentation submitted against the unit test-3 for the completion of M. Phil.A presentation submitted against the unit test-3 for the completion of M. Phil. In Library & Information Science, Second Semester (Session 2010-11)In Library & Information Science, Second Semester (Session 2010-11) Submitted by:Submitted by: Guided By:Guided By: Ritesh Tiwari Mr. Bhupendra RathaRitesh Tiwari Mr. Bhupendra Ratha Roll No. 16 Lecturer (SLIS, UTD, DAVV)Roll No. 16 Lecturer (SLIS, UTD, DAVV)
- 2. ContentsContents What is Data MiningWhat is Data Mining Technical DefinitionTechnical Definition ConceptConcept Terms Included in Data MiningTerms Included in Data Mining Data WarehouseData Warehouse MetadataMetadata Example of MetadataExample of Metadata Metadata SchemaMetadata Schema Processes included in Data MiningProcesses included in Data Mining Need of Data MiningNeed of Data Mining Applications of Data Mining in LibrariesApplications of Data Mining in Libraries ConclusionConclusion ReferencesReferences
- 3. What is Data Mining ?What is Data Mining ? As we find the elements like, Diamond,As we find the elements like, Diamond, Coal etc. out of the mines, the process ofCoal etc. out of the mines, the process of finding the elements is called Mining,finding the elements is called Mining, same way when we mine the exactsame way when we mine the exact required data or information from a largerequired data or information from a large amount of data, this process is known asamount of data, this process is known as Data MiningData Mining..
- 4. Technical Definition:Technical Definition: Data mining, a branch of ComputerData mining, a branch of Computer Science, is the process ofScience, is the process of extracting patterns from large dataextracting patterns from large data sets by combining methods fromsets by combining methods from Statistics and artificial IntelligenceStatistics and artificial Intelligence with database management. Itwith database management. It simply means finding the requiredsimply means finding the required information out of the huge datainformation out of the huge data sets.sets.
- 5. Concept:Concept: The subject “ Data Mining” is a partThe subject “ Data Mining” is a part of Computer Science which is allof Computer Science which is all about creating a system forabout creating a system for capturing the Data or information,capturing the Data or information, Editing, Formatting, Storage,Editing, Formatting, Storage, Naming, Indexing or Meta DataNaming, Indexing or Meta Data Creation, Preservation, SecurityCreation, Preservation, Security and Maintenance of same in such aand Maintenance of same in such a manner that makes the searching /manner that makes the searching / retrieval of data / Informationretrieval of data / Information effective and user friendly. Dataeffective and user friendly. Data Mining is a Broad term whichMining is a Broad term which includes so many processes toincludes so many processes to achieve the goal of fast & effectiveachieve the goal of fast & effective retrieval of Information / Data.retrieval of Information / Data.
- 6. Terms included in Data Mining:Terms included in Data Mining: Data Warehouse:Data Warehouse: Meta Data:Meta Data:
- 7. Data Warehouse:Data Warehouse: Data warehousing is combining data fromData warehousing is combining data from multiple and usually varied sources intomultiple and usually varied sources into one comprehensive and easilyone comprehensive and easily manipulated database. Commonmanipulated database. Common accessing systems of data warehousingaccessing systems of data warehousing include, queries analysis and reporting.include, queries analysis and reporting. Because data warehousing creates oneBecause data warehousing creates one database in the end, the number ofdatabase in the end, the number of sources can be anything you want it to be,sources can be anything you want it to be, provided that the system can handle theprovided that the system can handle the volume, of course. The final result,volume, of course. The final result, however, is homogeneous data, which canhowever, is homogeneous data, which can be more easily manipulated.be more easily manipulated.
- 8. Metadata:Metadata: Metadata is structured data whichMetadata is structured data which describes the characteristics of adescribes the characteristics of a resource. It shares many similarresource. It shares many similar characteristics to the cataloguing thatcharacteristics to the cataloguing that takes place in libraries, museums andtakes place in libraries, museums and archives. The term "meta" derives fromarchives. The term "meta" derives from the Greek word denoting a nature of athe Greek word denoting a nature of a higher order or more fundamental kind. Ahigher order or more fundamental kind. A metadata record consists of a number ofmetadata record consists of a number of pre-defined elements representing specificpre-defined elements representing specific attributes of a resource, and each elementattributes of a resource, and each element can have one or more values.can have one or more values.
- 9. Example of Metadata:Example of Metadata: Element nameElement name ValueValue Title: Web catalogueTitle: Web catalogue Creator: Dagnija McAuliffeCreator: Dagnija McAuliffe Publisher: University of QueenslandPublisher: University of Queensland LibraryLibrary Format: Text/htmlFormat: Text/html Relation: Library Web siteRelation: Library Web site
- 10. Metadata Schema:Metadata Schema: The Format or Schema of MetadataThe Format or Schema of Metadata may be vary in differentmay be vary in different organizations according to theirorganizations according to their requirements.requirements. Each metadata schema will usuallyEach metadata schema will usually have the following characteristics:have the following characteristics: A limited number of elementsA limited number of elements The name of each elementThe name of each element The meaning of each elementThe meaning of each element Location or Address of eachLocation or Address of each
- 11. Metadata Schema:Metadata Schema: Some of the most popular metadataSome of the most popular metadata schemas include:schemas include: Dublin CoreDublin Core AACR2AACR2 (Anglo-American Cataloging(Anglo-American Cataloging Rules)Rules) GILSGILS (Government Information Locator(Government Information Locator Service)Service) EADEAD (Encoded Archives Description)(Encoded Archives Description) IMSIMS (IMS Global Learning Consortium)(IMS Global Learning Consortium) AGLSAGLS (Australian Government Locator(Australian Government Locator
- 12. Processes included in Data Mining:Processes included in Data Mining: Creating the Databases.Creating the Databases. Integrating the different databases.Integrating the different databases. Editing and formatting of Data forEditing and formatting of Data for creating Data Ware-House.creating Data Ware-House. Organizing the data in helpful sequenceOrganizing the data in helpful sequence systematically.systematically. Proper naming of the data/document.Proper naming of the data/document. Creating the Meta data.Creating the Meta data. Searching/Retrieving the required andSearching/Retrieving the required and useful information or data out of the datauseful information or data out of the data ware-house with the help of Meta data.ware-house with the help of Meta data. Requesting the users for their feedback.Requesting the users for their feedback. Evaluation of system.Evaluation of system. Making Arrangements for Modification.Making Arrangements for Modification.
- 13. Need of Data Mining:Need of Data Mining: In earlier time “Availability of Information” was theIn earlier time “Availability of Information” was the greatest Problem of Researchers or Library users, butgreatest Problem of Researchers or Library users, but now a days “Huge quantity of Information” is thenow a days “Huge quantity of Information” is the greatest problem. The solution is needed for searchinggreatest problem. The solution is needed for searching the required information from this Huge amount ofthe required information from this Huge amount of Information, that’s why “Information, that’s why “ Data MiningData Mining” came in to” came in to existence to provide the solution to this situation.existence to provide the solution to this situation. Some other needs of “Data Mining” are as follows:Some other needs of “Data Mining” are as follows: For satisfying the user’s need.For satisfying the user’s need. For better Storage and Retrieval System especially inFor better Storage and Retrieval System especially in digital Environment.digital Environment. For increasing accuracy in search.For increasing accuracy in search. For preventing users from data-garbage.For preventing users from data-garbage. For solving the problems raised due to InformationFor solving the problems raised due to Information Explosion.Explosion. For creating user friendly search environment.For creating user friendly search environment. For saving the time of readers.For saving the time of readers. For providing global access to Information.For providing global access to Information.
- 14. Application of Data Mining in Libraries:Application of Data Mining in Libraries: The Data Mining is a broader term and includes “DataThe Data Mining is a broader term and includes “Data Warehousing and “Meta data creation” and now a daysWarehousing and “Meta data creation” and now a days applies in Libraries and Information Science in followingapplies in Libraries and Information Science in following applications:applications: In capturing the Information or data.In capturing the Information or data. In creating the Databases.In creating the Databases. In Integrating the Database.In Integrating the Database. In Proper naming of data or Information.In Proper naming of data or Information. In indexing and cataloguing of digitalIn indexing and cataloguing of digital documents.documents. In searching the Information out of the hugeIn searching the Information out of the huge data.data. In improving the services of libraries andIn improving the services of libraries and Information Centers.Information Centers.
- 15. Conclusion:Conclusion: Data Mining including Data WarehousingData Mining including Data Warehousing and Metadata creation is making theand Metadata creation is making the modern libraries capable of creating amodern libraries capable of creating a better Information Storage & Retrievalbetter Information Storage & Retrieval system, especially in the Digital Era.system, especially in the Digital Era. “Unavailability of Information” is not a“Unavailability of Information” is not a problem now days but “Huge amount ofproblem now days but “Huge amount of Information” is the problem rises due toInformation” is the problem rises due to “Information Explosion”. It is very difficult“Information Explosion”. It is very difficult to find a specific Information or data out ofto find a specific Information or data out of the Huge amount of Information or Data.the Huge amount of Information or Data. Data Mining helped the Libraries &Data Mining helped the Libraries & Information Centers to handle InformationInformation Centers to handle Information explosion and to face this situation.explosion and to face this situation.
- 16. References:References: Data Mining: concepts & techniques by Han andData Mining: concepts & techniques by Han and Kamber Publisher: ElsevierKamber Publisher: Elsevier Data Mining: introductory & advanced topics byData Mining: introductory & advanced topics by Dunham & Sridhar Publisher: PearsonDunham & Sridhar Publisher: Pearson EducationEducation http://www.wiseqeek.comhttp://www.wiseqeek.com http://www.library.uq.edu.auhttp://www.library.uq.edu.au http://en.wikipedia.orghttp://en.wikipedia.org
Editor's Notes
- #5: http://en.wikipedia.org/wiki/Data_mining#cite_note-brittanica-0
- #8: http://www.wisegeek.com/what-is-data-warehousing.htm
- #10: http://www.library.uq.edu.au/iad/ctmeta4.html
- #11: http://www.library.uq.edu.au/iad/ctmeta4.html
- #12: http://www.library.uq.edu.au/iad/ctmeta4.html