Data Mining

Outlier are

(i) legitimate data objects
(ii) illegitimate data objects
(iii) legitimate and illegitimate data objects
(iv) None of the above

Cluster is

(i) group of similar objects that differ significantly from other objects
(ii) operations on a database to transform or simplify data in order to prepare it for a machine learning algorithm
(iii) symbolic representation of facts or ideas from which information can potentially be extracted
(iv) None of the above

List at least four general characteristics of hierarchical clustering methods.

Compare and contrast k-medoids with k-means.

Which is a partitioning clustering method?

(i) Apriori
(ii) ID3
(iii) Naïve Bayes
(iv) k-Means

Some telecommunication companies want to segment their customers into distinct groups in order to send appropriate subscription offers. This is an example of

(i) supervised learning
(ii) data extraction
(iii) serration
(iv) unsupervised learning

Differentiate between Partitioning and Hierarchical Clustering methods. Explain Outlier Analysis and its importance in data mining.

Briefly describe the following approaches to clustering:

• partitioning methods
• hierarchical methods
• density-based methods
• grid-based methods
• model-based methods
• methods for high-dimensional data
• constraint-based methods

Give examples in each case.

What are the main requirements for cluster analysis?

Explain different basic clustering methods.

Explain DBSCAN briefly.

Distinguish between noise and outliers.

(i) Is noise ever interesting or desirable? Outliers?
(ii) Can noise objects be outliers?
(iii) Can noise make a typical value into an unusual one or vice versa?

Define Clustering. Discuss Model-Based Clustering techniques.