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Introducing the Paper:
Advancing Knowledge Discovery and Data
Mining
Ryota Eisaki
January 12, 2021
Author: Qi Luo
Publisher: IEEE
Date of Conference: 23-24 Jan. 2008
https://ieeexplore-ieee-org.ezproxy.tulips.tsukuba.ac.jp/document/
4470338
1
Abstract
The relation between Knowledge and Data Mining, and Knowledge
Discovery in Database(KDD) process are presented in the paper.
Data mining theory, Data mining tasks, Data Mining technology
and Data Mining challenges are also proposed.
2
1. Introduction
An important Knowledge Discovery and Data Mining goal is to
"turn data into knowledge".
Knowledge Discovery in Database(KDD) process:
1. Data selection
2. Data cleaning
3. Data transformation
4. Pattern searching (data mining)
5. Finding presentation, interpretation, and evaluation
3
2. What is Data Mining
Generally, data mining is the process of analyzing data from
different perspectives and summarizing it into useful information.
Technically, data mining is is the process of finding correlations or
patterns among dozens of fields in large relational databases.
• Operational or transactional data
- sales, costs, inventory, payroll, accounting
• No operational data
- industry sales, forecast data, and macro economic data
• Metadata
- logical database design or data dictionary definitions
4
2. What is Data Mining
The patterns, associations, or relationships among all this data can
provide information.
Information can converted into knowledge about historical patterns
and future trends.
Data warehousing is defined as a process of centralized data
management and retrieval.
5
3. Data Mining Tasks
Tasks in data mining can be classified as below:
1. Summarization
- abstraction or generalization of data
2. Classification
- derives a function or model which determines the class of an
object based on its attributes
3. Clustering
- identifies classes-also called clusters or groups-for a set of
objects whose classes are unknown
4. Association and trend analysis
6
4. Data Mining Technology
1. Association Rules
- the resulting associations can be used to filter the
information for human analysis and possibly to define a
prediction model based on observed behavior
2. Artificial Neural Networks
- the automatic learning framework as universal
approximations, with massively parallel computing character
and good generalization capabilities
3. Statistical Techniques
- includes linear regressions, discriminate analysis, or statistical
summarization
4. Machine Learning
- basically concerned with the automatic design of if-then rules.
7
5. Applicationi
1. Business application
- Data mining has been used in database marketing, retail data
analysis, stock selection, credit approval
2. Science application
- Data mining techniques have been used in astronomy,
molecular biology, medicine, geology and many more.
3. Other application
- used in health care management, tax fraud detection, many
laundering monitoring, and even sports.
8
6. Conclusion
Challenges:
1. Developing new algorithms
- classification, clustering, dependency analysis, and change
and deviation detection that scale to large databases
2. Develop effective means for data sampling, data reduction
3. Develop schemes capable of mining over no homogenous data
sets(including mixtures of multimedia, video, and text
modalities)
4. Developing new mining and search algorithms capable of
extracting more complex relationships between fields and able
to account for structure over fields
9

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Advancing Knowledge Discovery and Data Mining

  • 1. Introducing the Paper: Advancing Knowledge Discovery and Data Mining Ryota Eisaki January 12, 2021 Author: Qi Luo Publisher: IEEE Date of Conference: 23-24 Jan. 2008 https://ieeexplore-ieee-org.ezproxy.tulips.tsukuba.ac.jp/document/ 4470338 1
  • 2. Abstract The relation between Knowledge and Data Mining, and Knowledge Discovery in Database(KDD) process are presented in the paper. Data mining theory, Data mining tasks, Data Mining technology and Data Mining challenges are also proposed. 2
  • 3. 1. Introduction An important Knowledge Discovery and Data Mining goal is to "turn data into knowledge". Knowledge Discovery in Database(KDD) process: 1. Data selection 2. Data cleaning 3. Data transformation 4. Pattern searching (data mining) 5. Finding presentation, interpretation, and evaluation 3
  • 4. 2. What is Data Mining Generally, data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Technically, data mining is is the process of finding correlations or patterns among dozens of fields in large relational databases. • Operational or transactional data - sales, costs, inventory, payroll, accounting • No operational data - industry sales, forecast data, and macro economic data • Metadata - logical database design or data dictionary definitions 4
  • 5. 2. What is Data Mining The patterns, associations, or relationships among all this data can provide information. Information can converted into knowledge about historical patterns and future trends. Data warehousing is defined as a process of centralized data management and retrieval. 5
  • 6. 3. Data Mining Tasks Tasks in data mining can be classified as below: 1. Summarization - abstraction or generalization of data 2. Classification - derives a function or model which determines the class of an object based on its attributes 3. Clustering - identifies classes-also called clusters or groups-for a set of objects whose classes are unknown 4. Association and trend analysis 6
  • 7. 4. Data Mining Technology 1. Association Rules - the resulting associations can be used to filter the information for human analysis and possibly to define a prediction model based on observed behavior 2. Artificial Neural Networks - the automatic learning framework as universal approximations, with massively parallel computing character and good generalization capabilities 3. Statistical Techniques - includes linear regressions, discriminate analysis, or statistical summarization 4. Machine Learning - basically concerned with the automatic design of if-then rules. 7
  • 8. 5. Applicationi 1. Business application - Data mining has been used in database marketing, retail data analysis, stock selection, credit approval 2. Science application - Data mining techniques have been used in astronomy, molecular biology, medicine, geology and many more. 3. Other application - used in health care management, tax fraud detection, many laundering monitoring, and even sports. 8
  • 9. 6. Conclusion Challenges: 1. Developing new algorithms - classification, clustering, dependency analysis, and change and deviation detection that scale to large databases 2. Develop effective means for data sampling, data reduction 3. Develop schemes capable of mining over no homogenous data sets(including mixtures of multimedia, video, and text modalities) 4. Developing new mining and search algorithms capable of extracting more complex relationships between fields and able to account for structure over fields 9