Page 158 - DCAP208_Management Support Systems

Unit 9: Data Mining

Multimedia databases include video, images, audio and text media. Notes
There are two types of data mining tasks: descriptive data mining tasks that describe the
general properties of the existing data, and predictive data mining tasks that attempt to do
predictions based on inference on available data.

9.4 Keywords

Association Analysis: Association analysis is the discovery of what are commonly called association
rules.
Data Warehouse: A data warehouse as a storehouse, is a repository of data collected from
multiple data sources and is intended to be used as a whole under the same unified schema.

Flat Files: Flat files are simple data files in text or binary format with a structure known by the
data mining algorithm to be applied.
KDD: Knowledge Discovery in Databases (KDD) refers to the nontrivial extraction of implicit,
previously unknown and potentially useful information from data in databases.
Multimedia Database: Multimedia databases include video, images, audio and text media.
Relational Database: A relational database consists of a set of tables containing either values of
entity attributes, or values of attributes from entity relationships.
Spatial Databases: Spatial databases are databases that, in addition to usual data, store
geographical information like maps, and global or regional positioning.
Time-series Databases: Time-series databases contain time related data such stock market data
or logged activities.
Transaction Database: A transaction database is a set of records representing transactions, each
with a time stamp, an identifier and a set of items.

9.5 Review Questions

1. Explain the concept of data mining with example.
2. Discuss the different types of information collected in digital form in databases and in flat
files.

3. Discuss the concept of Data Mining and Knowledge Discovery.
4. Describe the steps included in the Knowledge Discovery process.
5. Make distinction between relational database and transaction database.
6. Explain the functionalities of data mining.
7. Discuss association analysis with example.

8. Illustrate the categorization of data mining systems.
9. Describe various issues in Data Mining.
10. Explain some application areas of data mining.

Answers: Self Assessment

1. Knowledge-Discovery 2. Spam filtering

LOVELY PROFESSIONAL UNIVERSITY 151