1.What
are the classifications of tools for data mining?
•
Commercial Tools
•
Public domain Tools
•
Research prototypes
2.What
are commercial tools?
Commercial
tools can be defined as the following products and usually are
associated
with the consulting activity by the same company:
1.
‘Intelligent Miner’ from IBM
2.
‘SAS’ System from SAS Institute
3.
‘Thought’ from Right Information Systems. Etc
3.
What are Public domain Tools?
Public
domain Tools are largely freeware with just registration fees:
’Brute’
from University
of Washington. ‘MC++’
from Stanford university, Stanford,
California.
4.
What are Research prototypes?
Some
of the research products may find their way into commercial
market:
‘DB Miner’ from Simon Fraser University, British Columbia, ‘Mining Kernel
System’
from University of Ulster, North Ireland.
5.What
is the difference between generic single-task tools and generic multi-task
tools?
Generic
single-task tools generally use neural networks or decision trees.
They
cover only the data mining part and require extensive pre-processing and
postprocessing
steps.
Generic
multi-task tools offer modules for pre-processing and postprocessing
steps
and also offer a broad selection of several popular data mining
algorithms
as clustering.
6.
What are the areas in which data warehouses are used in present and in future?
The
potential subject areas in which data ware houses may be developed at
present
and also in future are
1.Census
data:
The
registrar general and census commissioner of India decennially
compiles
information of all individuals, villages, population groups, etc. This
information
is
wide ranging such as the individual slip. A compilation of information of
individual
households,
of which a database of 5%sample is maintained for analysis. A data
warehouse
can be built from this database upon which OLAP techniques can be applied,
Data
mining also can be performed for analysis and knowledge discovery
2.Prices
of Essential Commodities
The
ministry of food and civil supplies, Government of India complies
daily
data for about 300 observation centers in the entire country on the prices of
essential
commodities such as rice, edible oil etc, A data warehouse can be built
for
this data and OLAP techniques can be applied for its analysis
7.
What are the other areas for Data warehousing and data mining?
•
Agriculture
•
Rural development
•
Health
•
Planning
•
Education
•
Commerce and Trade
8.
Specify some of the sectors in which data warehousing and data mining are used?
•
Tourism
•
Program Implementation
•
Revenue
•
Economic Affairs
•
Audit and Accounts
9.
Describe the use of DBMiner.
Used
to perform data mining functions, including characterization,
association,
classification, prediction and clustering.
10.
Applications of DBMiner.
The
DBMiner system can be used as a general-purpose online analytical
mining
system for both OLAP and data mining in relational database and
datawarehouses.
Used
in medium to large relational databases with fast response time.
11.
Give some data mining tools.
DBMiner
GeoMiner
Multimedia
miner
WeblogMiner
12.
Mention some of the application areas of data mining
DNA
analysis
Financial
data analysis
Retail
Industry
Telecommunication
industry
Market
analysis
Banking
industry
Health
care analysis.
13.
Differentiate data query and knowledge query
A
data query finds concrete data stored in a database and corresponds to a
basic
retrieval statement in a database system.
A
knowledge query finds rules, patterns and other kinds of knowledge in a
database
and corresponds to querying database knowledge including
deduction
rules, integrity constraints, generalized rules, frequent patterns and
other
regularities.
14.Differentiate
direct query answering and intelligent query answering.
Direct
query answering means that a query answers by returning exactly what
is
being asked.
Intelligent
query answering consists of analyzing the intent of query and
providing
generalized, neighborhood, or associated information relevant to the
query.
15.
Define visual data mining
Discovers
implicit and useful knowledge from large data sets using data and/
or
knowledge visualization techniques.Integration
of data visualization and data mining.
16.
What does audio data mining mean?
Uses
audio signals to indicate patterns of data or the features of data mining
results.Patterns
are transformed into sound and music.
To
identify interesting or unusual patterns by listening pitches, rhythms, tune
and
melody.
Steps
involved in DNA analysis
Semantic
integration of heterogeneous, distributed genome databases
Similarity
search and comparison among DNA sequences
Association
analysis: Identification of co-occuring gene sequences
Path
analysis: Linking genes to different stages of disease development
Visualization
tools and genetic data analysis
17.What
are the factors involved while choosing data mining system?
Data
types
System
issues
Data
sources
Data
Mining functions and methodologies
Coupling
data mining with database and/or data warehouse systems
Scalability
Visualization
tools
Data
mining query language and graphical user interface.
18.
Define DMQL
Data
Mining Query Language
It
specifies clauses and syntaxes for performing different types of data mining
tasks
for example data classification, data clustering and mining association
rules.
Also it uses SQl-like syntaxes to mine databases.
19.
Define text mining
Extraction
of meaningful information from large amounts free format textual
data.
Useful
in Artificial intelligence and pattern matching
Also
known as text mining, knowledge discovery from text, or content
analysis.
20.
What does web mining mean
Technique
to process information available on web and search for useful data.
To
discover web pages, text documents , multimedia files, images, and other
types
of resources from web.
Used
in several fields such as E-commerce, information filtering, fraud
detection
and education and research.
21.Define
spatial data mining.
Extracting
undiscovered and implied spatial information.
Spatial
data: Data that is associated with a location
Used
in several fields such as geography, geology, medical imaging etc.
22.
Explain multimedia data mining.
Mines
large data bases.
Does
not retrieve any specific information from multimedia databases
Derive
new relationships , trends, and patterns from stored multimedia data
mining.
Used
in medical diagnosis, stock markets ,Animation industry, Airline
industry, Traffic management systems, Surveillance
systems etc.