Data Warehousing And Mining

Explain the steps involved in KDD process with a neat sketch.

Discuss the data visualization and data transformation with suitable examples.

Define data mining. Explain the major issues in Data Mining.

A database has five transactions given below. Let minsup = 70% and minconf = 90%.
Determine the frequent items using Apriori algorithm. List all the strong association rules.

Consider the following data set and implement the K-Medioid algorithm.
Also, Find the total cost, distances and plot the scatter graph.

Describe DBSCAN algorithm with a suitable example.

Construct a decision tree with root node intensity from the data in the table below. The first row contains attribute names. Each row after the first represents the values for one data instance. The output attribute is Group.

List and explain the applications of web mining in detail.

Discuss with different types of methods to identify and analyze the web usage patterns with examples.

Define episode discovery. Determine the complex sequence and simple sequence by considering the following event sequence.

`(r_1, {d}), (r_2, {b}), (r_3, {a,c}), (r_4, {c}), (r_5, {c,d}), (r_6, {d}), (r_10, {a}), (r_13, {b})`

Find a complete set of frequent subsequences of the following given set of sequences.

Discuss the different types of trends of spatial mining with suitable examples.

Enumerate the steps involved in visual data exploration with neat sketches.

Give the difference between text-based image retrieval and content-based image retrieval.

Write a short note on any two of the following:

1. Challenges in DM
2. Partitioning algorithm - CLARANS
3. Web Content Mining
4. WUM algorithm