Rapid Privacy Preserving Algorithm for Large Databases

2009 ◽  
pp. 1050-1061
Author(s):  
K. Anbumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Large Databases ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision- making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Improved Privacy

2011 ◽  
pp. 295-316
Author(s):  
K. Abumani ◽  
R. Nedunchezhian
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Empirical Analysis ◽  
Time Complexity ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Sensitive Information ◽  
Data Mining Techniques ◽  
Novel Method ◽  
Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision-making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Mining Frequent Closed Itemsets for Association Rules

2009 ◽  
pp. 537-546
Author(s):  
Anamika Gupta ◽  
Shikha Gupta ◽  
Naveen Kumar
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Marketing Strategies ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Rule Mining ◽  
Market Basket ◽  
Large Databases ◽  
Very Large Databases ◽  
Closed Itemsets

Association refers to correlations that exist among data. Association Rule Mining (ARM) is an important data-mining task. It refers to discovery of rules between different sets of attributes/items in very large databases (Agrawal R. & Srikant R. 1994). The discovered rules help in strategic decision making in both commercial and scientific domains. A classical application of ARM is market basket analysis, an application of data mining in retail sales where associations between the different items are discovered to analyze the customer’s buying habits in order to develop better marketing strategies. ARM has been extensively used in other applications like spatial-temporal, health care, bioinformatics, web data etc (Han J., Cheng H., Xin D., & Yan X. 2007).

Data Mining Fundamental Concepts and Critical Issues

2011 ◽  
pp. 418-423
Author(s):  
John Wang ◽  
Qiyang Chen ◽  
James Yao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Critical Issues ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

Quality and Effectiveness of ERP Software

2020 ◽  
pp. 28-52
Author(s):  
Stephen Makau Mutua ◽  
Raphael Angulu
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Knowledge Discovery ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Data Repository ◽  
Quality Information ◽  
Erp System ◽  
Entry Points ◽  
Over Time

Over time, the adoption of ERP systems has been wide across many small, medium, and large organizations. An ERP system is supposed to inform the strategic decision making of the organization; therefore, the information drawn from the ERP system is as important as the data stored in it. Poor data quality affects the quality information in it. Data mining is used to discover trends and patterns of an organization. This chapter looks into the way of integrating these data mining into an ERP system. This is conceptualized in three crucial views namely the outer, inner, and the knowledge discovery view. The outer view comprises of the collection of various entry points, the inner view contains the data repository, and the knowledge discovery view offers the data mining component. Since the focus is data mining, the two strategies of supervised and unsupervised are discussed. The chapter then concludes by presenting the probable problems within which each of these two strategies (classification and clustering) can be put into place within the mining process of an ERP system.

Expanding Data Mining Power with System Dynamics

2008 ◽  
pp. 2688-2696
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Pattern Recognition ◽  
System Dynamics ◽  
Knowledge Discovery ◽  
Strategic Decision ◽  
Knowledge Discovery In Databases ◽  
Strategic Decision Making ◽  
The World ◽  
Mathematical Techniques

Business intelligence (BI) is a key topic in business today, since it is focused on strategic decision making and on the search of value from business activities through empowering a “forward-thinking” view of the world. From this perspective, one of the most valuable concepts within BI is the “knowledge discovery in databases” or “data mining,” defined as “the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (SPSS, 1997).

Expanding Data Mining Power with System Dynamics

2005 ◽  
pp. 1155-1161
Author(s):  
Edilberto Casado
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Pattern Recognition ◽  
System Dynamics ◽  
Knowledge Discovery ◽  
Strategic Decision ◽  
Knowledge Discovery In Databases ◽  
Strategic Decision Making ◽  
The World ◽  
Mathematical Techniques

Business intelligence (BI) is a key topic in business today, since it is focused on strategic decision making and on the search of value from business activities through empowering a “forward-thinking” view of the world. From this perspective, one of the most valuable concepts within BI is the “knowledge discovery in databases” or “data mining,” defined as “the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (SPSS, 1997).

Data Mining

2011 ◽  
pp. 167-172
Author(s):  
Richard Peterson
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Classification Model ◽  
Data Warehouses ◽  
Business Decisions ◽  
Large Databases ◽  
Mining Tools

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.

Internet Data Mining Using Statistical Techniques

2008 ◽  
pp. 1446-1453
Author(s):  
Kuldeep Kumar
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Techniques ◽  
The Internet ◽  
Broad Area ◽  
Financial Applications ◽  
Large Databases ◽  
Mining Tools

Data mining has emerged as one of the hottest topics in recent years. It is an extraordinarily broad area and is growing in several directions. With the advancement of the Internet and cheap availability of powerful computers, data is flooding the market at a tremendous pace. However, the technology for navigating, exploring, visualising, and summarising large databases is still in its infancy. Internet data mining is the process of collecting, analysing, and decision making while the data is being collected on the Internet. In most of the financial applications, data is updated every second or minute or hour. By using Internet data-mining tools, a decision can be made as soon as data are updated.

Internet Data Mining Using Statistical Techniques

2005 ◽  
pp. 1629-1634
Author(s):  
Kuldeep Kumar
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Techniques ◽  
The Internet ◽  
Broad Area ◽  
Financial Applications ◽  
Large Databases ◽  
Mining Tools

Data mining has emerged as one of the hottest topics in recent years. It is an extraordinarily broad area and is growing in several directions. With the advancement of the Internet and cheap availability of powerful computers, data is flooding the market at a tremendous pace. However, the technology for navigating, exploring, visualising, and summarising large databases is still in its infancy. Internet data mining is the process of collecting, analysing, and decision making while the data is being collected on the Internet. In most of the financial applications, data is updated every second or minute or hour. By using Internet data-mining tools, a decision can be made as soon as data are updated.

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