Data Mining (IT-603 (B)) - Important Questions

Describe the steps involved in the knowledge discovery process (KDD) in data mining.

Compare ROLAP, MOLAP and OLAP. Explain different OLAP operations.

Explain data transformation strategies in detail.

In real world data, tuples with missing values for some attributes are a common occurrence. Describe various methods for handling this problem.

Draw a snowflake schema diagram for "University" data warehouse which consists of four dimensions student, course, semester and instructor and two measures count and avg_grade. At the lowest conceptual level, show the normalized dimension tables.

Explain data mining functionalities and classification of data mining systems.

Give an example showing that items in a strong association rule can be negatively correlated.

Apriori algorithm makes use of prior knowledge of subset support properties. Prove that all non-empty subsets of a frequent itemset must also be frequent.

Explain why tree pruning is useful in decision tree induction and a drawback of using a separate validation set to evaluate pruning.

Give an example showing why k-means may not find the global optimum of within-cluster variation.

Explain a clustering-based outlier detection method that can detect outliers at different granularity levels in a cluster hierarchy.

Explain the advantage of using unlabeled objects in the training dataset for semi-supervised outlier detection.

What is Web mining? Explain its types.

Describe the security issues in data mining.

Describe spatial and temporal mining with suitable example.

Write the k-means algorithm with an example.