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What are three phases of a decision tree? Describe the significance of each of the phases.
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Write short notes on Outler analysis.
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Write briefly Datamining and privacy.
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Write briefly Datamining and query language.
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Write briefly Statistical tools in datamining techniques.
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Write in brief on the Www aspects of data mining.
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Write in brief on the Documents aspects of data mining.
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Write in brief on the Muitimedia data basis aspects of data mining,
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Write in brief on the Temporal (time sequence) data basis aspects of data mining,
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Write in brief on the Spatial data bases aspects of data mining,
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To establish system for Datamining is necessary, to first set up a data warehousing Discuss. Also, write various steps for preprocessing of data for application of datamining tools.
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How are data mining techniques useful as DSS? Discuss with examples.
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What is meta data? Describe various types used in DW and for what purpose?
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Write short notes on Issues related to classification and prediction.
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Describe the development of data cube technology. How does this technology make mining process more efficient?
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Discuss concept of data mart.
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Describe importance of initial data cleaning for use in data mining.
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What are data mining primitives? Discuss their applications in brief.
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Data mining is also termed as Knowledge Discovery from Data (KDD).
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Mining associated rules, their concept and applications.
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Cluster analysis and its application in data mining.
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Describe OLAP tools. What is ROLAP?
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What is data mining? How is it different from conventional query approach in DBMS?
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Differentiate legacy data base and operational database.
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Differentiate data base and data ware housing. How DW concepts make it superior to conventional DBMS.
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a. With an example, explain frequent item set generation in FP growth algorithm.
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Write short note on: d. Text clustering
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Write short note on: c. Mining spatial and temporal data bases.
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Write short note on: b. Web content mining.
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Write short note on: a. Applications of data mining.
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c. Explain K-means clustering method.
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b. Explain different methods for computing distances between clusters.
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a. What is cluster analysis? Compare it with classification.
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b. Explain how the predictive accuracy of classification methods be estimated.
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a. Explain Bayesian classification.
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b. Explain the K-Nearest Neighbor classification algorithm. What are characteristics of nearest neighbor classifier?
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a. How decision trees are used for classification? Explain decision tree induction algorithm for classification
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b. Develop an algorithm for rule generation using Apriori algorithm.
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Describe data integration and data reduction.
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c. List and explain the different types of similarity measures of binary data.
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b. Define data preprocessing. List the different strategies involved in it.
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a. Explain the different types of data sets with examples.
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c. Explain the different guidelines for OLAP implementation.
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b. Describe Codd's OLAP characteristics.
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a. List out the differences between ROLAP and MOLAP.
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b. What is ETL? List out the major issues to be resolved during the implementation of ETL.
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a. Define ODS. Explain the architecture of ODS, with diagram.
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Write short notes on Trends in research in datamining and its applications.
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Write short notes on Datamining and visualization.
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Write short notes on Datamining and CRM (Customer Relation Management).
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Differentiate between OLAP and OLTP.
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Write note on Correlation analysis.
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Write note on Supervised learning vs. unsupervised learning.
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Write note on Data Transformation.
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What is cluster analysis? Discuss major clustering methods.
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Compare the advantages and disadvantages of eager classification e.g decision tree, neural network) versus lazy classification (e.g., k-
nearest neighbor, case-based reasoning).
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Discuss the steps of decision tree classification.
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Discuss the term data cleaning. What are different methods for handling the missing values?
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What are the applications of data mining in marketing?
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What are the essentials steps of data mining?
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Discuss the benefits of data mining.
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Differentiate between Classification and Prediction.
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Differentiate between OLAP and data mining.
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What is data warehousing and why it is important for decision support?
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Discuss the architecture for data warehouse?
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What are the features of a data warehouse? Explain each feature by taking an example.
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What do you understand by back propagation? Discuss.
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What is difference between data, information and knowledge?
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What is difference between data warehouse and data mart? Discuss.
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What do you understand by outlier analysis?
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Discuss different OLAP operations.
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Define the terms: Discrimination and characterization.
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Discuss the term support and confidence used in association rule mining,
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What do you mean by the term Hierarchy? Discuss.
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Disquss different types of data on which data mining can be applied.
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Differentiate between the OLAP and OLAM.
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Write note on Lazy learners.
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Write note on Classification vs Prediction.
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Write note on Temporal database vs Sequential database.
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Explain the design and construction of Data warehouse in detail.
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List the different categorization of OLAP tools.
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How would you measure the quality of Cluster? Explain.
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Discuss the social impact of Data mining detail.
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What are the major issues in data mining? Discuss.
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Explain decision tree in classification.
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What is back propogation? How is back propogation performed?
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Differentiate between the Classification and Prediction.
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Differentiate between the Data mining and KDD.
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What is metadata? Why it is important?
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Explain the architecture of a data mining system with suitable diagram.
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Discuss Data integration and Data transformation in detail.
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What is knowledge discovery in Database? Explain.
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Can a data mining system generate interesting patterns? Justify.
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Define support and confidence.
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What are the applications of Data mining? Discuss.
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Differentiate between Classification and Clustering.
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Data mining is multidisciplinary field. Discuss.
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What do you understand by frequent patterns? Explain.
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Explain different OLAP operations.
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What is deta preprocessing? Discuss.
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