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Data Warehouse Mining Viva

DWMBI (Data Warehouse Mining & Business Intelligence) Viva Questions:
Hey guys, welcome back we @ conceptSimplified are devoted to simplify your task. Following are few of the Most frequently asked Viva questions in DWMBI and would be extremely useful if you are specifically from Mumbai University.

Data Warehouse Mining Viva
CH-1 Introduction to Data Mining

  • What is data mining?
  • What are functionalities of Data Mining?
  • Classify Data Mining systems
  • Explain Integration of Data Mining system with a Database or Datawarehouse
  • Architecture of Typical Data mining System?
  • What are major Data Mining Issues?
  • Explain KDD process?

CH-2 Data WareHousing

  • What is Data warehousing?
  • Difference between Database and Datawarehouse?
  • Explain Star Schema?
  • Explain Snow flake Schema?
  • Difference between Star and snow flake schema?
  • Explain Fact and Dimensional table?
  • Explain Factless Fact table?
  • What is OLAP?
  • What is OLTP?
  • State applications of OLAP.
  • Difference between OLAP and OLTP?
  • State OLAP operations?

CH-3 Data Preprocessing

  • Difference between clustering and classification?
  • What is Data cleaning?
  • What is Data Integration?
  • What is Data Transformation?
  • State Data Reduction techniques ?
  • Explain Data Reduction techniques in short?
  • Which techniques are used for Numerosity Reduction?
  • Give ways of handling Noise Data?
  • What is noisy data?

CH-4 Mining Frequent Patterns,Associations and Correlations

  • What is Market basket analysis?
  • Explain Apriori Algorithm?
  • Explain FP-Tree Algorithm?
  • How FP-Tree is better than Apriori Algorithm?
  • Give formula of Support and Confidence?
  • K-mean Algorithm
  • What are Constraint based association rule mining?
  • Explain mining multilevel association Rules?
  • Explain mining multidimensional association Rules?

CH-5 Classification and Prediction

  • What is Classification?
  • What is Prediction?
  • State Issues regarding Classification and Prediction?
  • State various classification methods?
  • What is Regression?
  • State types of Regression?
  • Explain Linear Regression?
  • Explain Non Linear Regression?
  • Give formulae of a)Information Gain b)Entropy c)Gini index?
  • Explain Decision tree in brief?
  • Explain Bayesian classification?

CH-6 Cluster Analysis

  • What is clustering ?
  • What is Clustering Analysis?
  • State types of data in Clustering Analysis?
  • State Categories of clustering methods?
  • State partitioning methods?
  • What is BIRCH?
  • What is ROCK?
  • Explain DBSCAN?
  • Explain K-means?
  • Explain K-mediods?
  • Explain Agglomerative Clustering?
  • Explain Outliers Analysis?

CH-7 Mining Stream and Sequence Data

  • What is Stream Data?
  • Explain Association mining in stream data?
  • Explain Sequence Mining in transactional database?
  • Explain different Data Stream methodologies?
  • Explain Hoeffding Tree Algorithm?

CH-8 Spatial Data & Text Mining

  • Compare Data Mining and Text Mining.
  • State Spatial Clustering methods.
  • What is Spatial OLAP?
  • What is Spatial data mining?
  • State different approaches in Text Mining?
  • State Spatial Clustering Methods?
  • What is Web mining ?
  • What is Web Content Mining?
  • What is Web Structure Mining?

CH-9 Data Mining for Bussiness Intelligence Applications.

  • What is business intelligence ?
  • State Business Intelligence issues?
  • How Data Mining be used for BI Applications?
  • Explain Data Mining for Market Segmentation?
  • Explain Data Mining for Retail Industry?

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