Data Warehouse and Data Mining

(a) What is back propogation? How is back propogation performed?

Write the steps to mine spatial and multimedia databases.

Answer the following questions in brief:

(a) What do you mean by Frequent Pattern mining in databases?

What are the major issues in Data Mining? Explain in detail.

(b) Compare OLAP and OLTP systems.

(a) How are missing values and noisy data handled in data cleaning step of data mining? 

(a) What are typical OLAP operations? Explain in brief.

(b) Explain Apriori Algorithm of association rule mining.

(b) Explain k-means algorithm in detail with suitable diagram

(a) Differentiate between eager and lazy learners? Explain their different types.

(b) Explain k-means algorithm in detail with suitable diagram

(a) What are the major issues in data mining? Explain..

(b) What are data mining applications in Telecom industry?

(a) Discuss social impacts of data mining in detail.

Answer the following questions in brief:

(b) Define Quantile plot and scatter plot.

Answer the following questions in brief:

(c) What do you mean by data transformation? Explain.

Answer the following questions in brief:

(d) Define Regression technique for predictive analysis of data.

Answer the following questions in brief:

(c) Explain support and confidence in relation to association rule mining.

Answer the following questions in brief:

(f) What is a data warehouse?

Answer the following questions in brief:

(g) What is Starnet Query Model for querying multidimensional databases?

Answer the following questions in brief:

(h) What are the applications of data mining?

Answer the following questions in brief:

(i) What is accuracy and error measure in relation to classification?

Answer the following questions in brief:

(j) What ate data mining task primitives?

 Write short note on:

(i) Mining the World Wide Web

Write short note on:

(i) Mining spatial databases. 

(a) Discuss Data integration and Data transtormation in detail.

(b) Explain the architecture of a data mining system with suitable diagram.

(c) Classification and Prediction.

(b) Explain decision tree in classification.

(a) What are the major issues in data mining? Discuss.

(b) Discuss the social impact of Data mining detail.

a) How would you measure the quality or Cluster? Explain.

(b) List the different categorization of OLAP tools.

(a) Explain the design and construction of Data warehouse in detail.

Write note on any two the following:-

(a) Temporal database vs Sequential database.

(b) Classification vs Prediction.

(c) Lazy learners.

(a) Explain star, snowflake schema for multidimensional data models.

What is data preprocessing? Write and explain all the steps of data preprocessing.

What is Multidimensional Data Model? Explain in detall the architecture of data warehouse. 

What are frequent patterns, association and correlation? Explain with the help of a suitable example the advantages of frequent pattern mining.

What is Classification? Write and explain the decision tree induction method. 

What do you understand by Cluster Analysis? Explain Partitioning method in detail.  

Write the major applications of Data mining in today's world.

(b) how can you measure dispersion of data? Explain the concept of Range, Quartiles, Outliers, and Boxplots.

(b) Define Knowledge Discovery in Databases with suitable diagram.