ISDS 415 – Predictive Analytics I (Chapter 4)_ DataISDS
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
ISDS
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
Mining Process, Methods, and Algorithms _ Study Guide.pdf
ISDS 415 – Predictive
Analytics I (Chapter 4): Data
Mining Process, Methods,
and Algorithms | Study
Guide
Guidehttps://www.stuvia.com/dashboard!@_)#*)(@$)($@*($@)($@*_
ISDS 415 – Predictive Analytics I (Chapter 4)_ DataISDS
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
ISDS
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
Mining Process, Methods, and Algorithms _ Study Guide.pdf
,Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
Terms in this set (62)
are the ideas behind data mining new? when were they no 1980's
formed?
What are some reasons data mining has gained 1. more intense competition
recognition? 2. general rec. of the untapped value hidden in large data sources
3. consolidation and integration of database records
4. the exponential increase in data processing and storage tech
5. sig red in the cost of hardware and software for data storage and processing
6. movement towards the demassification - con of info resources into nonphysical
form of business practices.
what fields on the commercial side most commonly use finance
data mining? retail
healthcare
data mining term used to describe discovering or mining knowledge from large amounts of
data.
Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
, Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
terms that describe data mining 1. process- many iterative steps
2. nontrivial- experiment search or inference is involved
3. valid- discovered patterns should hold true on new data w/ a certain degree of
certainty
4. novel- patterns are not preciously known to the user w/in the context of they
system being analyzed
5. potentially useful- discovered patterns should lead to some benefit
6. ultimately understandable- the patterns should make make business sense
3 major characteristics and objectives of data mining? 1. data buried deep in very large databases. data is cleansed and consolidated into
a data warehouse. may be presented in a variety of forms
2. the data mining environment is usually a client/ server architecture or a web-
based IS architecture
3. sophisticated new tools
4. the miner is often the end user
5. striking it rich often involves finding an unexpected results and req end user to
think creatively
6. data mining tools are readily combined w/ spreadsheets and other software
development tools
7. b/c large amounts of data and massive search efforts is is sometimes necessary
to use parallel processing for data mining.
Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
ISDS
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
Mining Process, Methods, and Algorithms _ Study Guide.pdf
ISDS 415 – Predictive
Analytics I (Chapter 4): Data
Mining Process, Methods,
and Algorithms | Study
Guide
Guidehttps://www.stuvia.com/dashboard!@_)#*)(@$)($@*($@)($@*_
ISDS 415 – Predictive Analytics I (Chapter 4)_ DataISDS
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
ISDS
Mining
415Process,
– Predictive
Methods,
Analytics
and Algorithms
I (Chapter_4)_
Study
Data
Guide.pdf
Mining Process, Methods, and Algorithms _ Study Guide.pdf
,Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
Terms in this set (62)
are the ideas behind data mining new? when were they no 1980's
formed?
What are some reasons data mining has gained 1. more intense competition
recognition? 2. general rec. of the untapped value hidden in large data sources
3. consolidation and integration of database records
4. the exponential increase in data processing and storage tech
5. sig red in the cost of hardware and software for data storage and processing
6. movement towards the demassification - con of info resources into nonphysical
form of business practices.
what fields on the commercial side most commonly use finance
data mining? retail
healthcare
data mining term used to describe discovering or mining knowledge from large amounts of
data.
Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
, Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
terms that describe data mining 1. process- many iterative steps
2. nontrivial- experiment search or inference is involved
3. valid- discovered patterns should hold true on new data w/ a certain degree of
certainty
4. novel- patterns are not preciously known to the user w/in the context of they
system being analyzed
5. potentially useful- discovered patterns should lead to some benefit
6. ultimately understandable- the patterns should make make business sense
3 major characteristics and objectives of data mining? 1. data buried deep in very large databases. data is cleansed and consolidated into
a data warehouse. may be presented in a variety of forms
2. the data mining environment is usually a client/ server architecture or a web-
based IS architecture
3. sophisticated new tools
4. the miner is often the end user
5. striking it rich often involves finding an unexpected results and req end user to
think creatively
6. data mining tools are readily combined w/ spreadsheets and other software
development tools
7. b/c large amounts of data and massive search efforts is is sometimes necessary
to use parallel processing for data mining.
Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf Ch. 4 Predictive analytics I_ Data mining process, methods and algorithms ISDS 415.pdf
