DATA MINING WHAT IS DATA MINING
QUESTIONS AND ANSWERS
genetic algorithms - Answer-a level of data analysis. optimization techniques that
use processes such as genetic combination, mutation, natural selection in a design
based on the concepts of natural evolution..
decision trees - Answer-a level of data analysis. tree-shaped structures that
represent sets of decisions. rules for dataset classification. CART. CHAID. urels to
apply to new data to predict outcomes.
nearest neighbour method - Answer-a level of data analysis. classifies each record in
a dataset based on a combination of the classes of the k records.
rule induction - Answer-a level of data analysis. extraction of useful if-then rules from
data based on statistics.
data visualization - Answer-a level of data analysis. visual interpretation of complex
relationships in multidimensional data. graphic tools.
size of the database - Answer-the more data being processed and maintained the
more power an infrastructure system is needed
query complexity - Answer-the more complex the queries and the greater the
number of queries being processed the more powerful the system need be
MPP - Answer-massively parallel processors have order of magnitude in
improvements in query time.
data mining - Answer-date or knowledge discovery. is the process of analyzing data
from different perspectives and summarizing it into useful info.
useful information - Answer-information that can increase revenue, cut costs or both.
data mining software - Answer-analytical tool to use for analyzing data. analyze from
different dimensions, angles, categorize it and summarize relationships.
data mining and fundamentals - Answer-technically it is the process of finding
correlations or patterns among dozens of fields in a large relational database.
continuous innovation - Answer-technology of mining is not new. computer
processing power, disk storage and statistical software are increasing the accuracy
of data analysis and lowering costs.
continuous innovation: example - Answer-grocery chain. oracle to find local buying
patterns. bought diapers and beer. when they did weekly shopping. when they rarely
shopped. made an insight on buying beer for the coming week.
QUESTIONS AND ANSWERS
genetic algorithms - Answer-a level of data analysis. optimization techniques that
use processes such as genetic combination, mutation, natural selection in a design
based on the concepts of natural evolution..
decision trees - Answer-a level of data analysis. tree-shaped structures that
represent sets of decisions. rules for dataset classification. CART. CHAID. urels to
apply to new data to predict outcomes.
nearest neighbour method - Answer-a level of data analysis. classifies each record in
a dataset based on a combination of the classes of the k records.
rule induction - Answer-a level of data analysis. extraction of useful if-then rules from
data based on statistics.
data visualization - Answer-a level of data analysis. visual interpretation of complex
relationships in multidimensional data. graphic tools.
size of the database - Answer-the more data being processed and maintained the
more power an infrastructure system is needed
query complexity - Answer-the more complex the queries and the greater the
number of queries being processed the more powerful the system need be
MPP - Answer-massively parallel processors have order of magnitude in
improvements in query time.
data mining - Answer-date or knowledge discovery. is the process of analyzing data
from different perspectives and summarizing it into useful info.
useful information - Answer-information that can increase revenue, cut costs or both.
data mining software - Answer-analytical tool to use for analyzing data. analyze from
different dimensions, angles, categorize it and summarize relationships.
data mining and fundamentals - Answer-technically it is the process of finding
correlations or patterns among dozens of fields in a large relational database.
continuous innovation - Answer-technology of mining is not new. computer
processing power, disk storage and statistical software are increasing the accuracy
of data analysis and lowering costs.
continuous innovation: example - Answer-grocery chain. oracle to find local buying
patterns. bought diapers and beer. when they did weekly shopping. when they rarely
shopped. made an insight on buying beer for the coming week.
