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Data mining in the field of library

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Megha Goyal
Megha Goyal

KDD is the automatic extraction of hidden knowledge from large volumes of data. KDD in databases is the non-trivial process of identifying valid, potentially useful and ultimately understandable patterns in data. The document then discusses the steps of KDD which include data cleaning, integration, selection, transformation, mining, evaluation and presentation. It also discusses the goals of the knowledge discovery process and how a KDD system can be used in libraries for searching, classification and acquisition by developing domain knowledge, cleaning data, choosing mining tasks and consolidating discovered knowledge.

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KNOWLEDGE DISCOVERY DATABASE IN THE FIELD OF LIBRARY SCIENCE PRESENTED BY- MEGHA GOYAL
The knowledge discovery database • KDD is the automatic extraction of non-obvious, hidden knowledge from large volumes of data. • KDD in databases is the non-trivial process of identifying valid, potentially useful and ultimately understandable patterns in data Why do we need KDD • Data is the important tool to gain a competitive edge by providing improved, cutomized services.
Knowledge discovery database • KDD in databases is the non-trivial process of identifying valid, potentially useful and ultimately understandable patterns in data. 1. LIST OF STEPS OF KDD:- 2. Data Cleaning- in this step, the noise and inconsistent data is removed 3. Data integration-in this step, multiple data sources are combined. 4. Data selection-in this step, data relevant to the analysis task are retreived from the database. 5. Data transformation- in this step, data is transformed or consolidated into forms appropriate for mining by performing aggregation operations. 6. Data mining- intellingent methods are applied in order to extract data patterms. 7. Pattern evaluation- data patterns are evaluated 8. Knowledge presentation- knoledge is represented.
KDD PROCESS- List of steps
Knowledge discovery Process GOALS DATA SELECTION, ACQUISITION&INTEGRATION DATA REDUCTION AND PROJECTION MATCHING THE GOALS EXPLORATORY DATA ANALYSIS DATA MINING INTREPRETATION AND TESTING CONSOLIDATION AND USE
KDD System-in Library • In libraries online databases use KDD system • It can be used in searching, classification and acquisition process of libraries. • Developing an understanding of application domain helps in relevant prior knowledge. • Cleaning of processing of data • Choosing data mining task of KDD process like classification. • Consolidating discovered knowledge

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Data mining in the field of library

  • 1. KNOWLEDGE DISCOVERY DATABASE IN THE FIELD OF LIBRARY SCIENCE PRESENTED BY- MEGHA GOYAL
  • 2. The knowledge discovery database • KDD is the automatic extraction of non-obvious, hidden knowledge from large volumes of data. • KDD in databases is the non-trivial process of identifying valid, potentially useful and ultimately understandable patterns in data Why do we need KDD • Data is the important tool to gain a competitive edge by providing improved, cutomized services.
  • 3. Knowledge discovery database • KDD in databases is the non-trivial process of identifying valid, potentially useful and ultimately understandable patterns in data. 1. LIST OF STEPS OF KDD:- 2. Data Cleaning- in this step, the noise and inconsistent data is removed 3. Data integration-in this step, multiple data sources are combined. 4. Data selection-in this step, data relevant to the analysis task are retreived from the database. 5. Data transformation- in this step, data is transformed or consolidated into forms appropriate for mining by performing aggregation operations. 6. Data mining- intellingent methods are applied in order to extract data patterms. 7. Pattern evaluation- data patterns are evaluated 8. Knowledge presentation- knoledge is represented.
  • 5. Knowledge discovery Process GOALS DATA SELECTION, ACQUISITION&INTEGRATION DATA REDUCTION AND PROJECTION MATCHING THE GOALS EXPLORATORY DATA ANALYSIS DATA MINING INTREPRETATION AND TESTING CONSOLIDATION AND USE
  • 6. KDD System-in Library • In libraries online databases use KDD system • It can be used in searching, classification and acquisition process of libraries. • Developing an understanding of application domain helps in relevant prior knowledge. • Cleaning of processing of data • Choosing data mining task of KDD process like classification. • Consolidating discovered knowledge