Results 131 to 140 of about 63,468 (158)

Hiding Fuzzy Association Rules in Quantitative Data

2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops, 2008
Data mining and knowledge discovery from databases are researches in which unknown associations automatically discovered from big amounts of data. Advances in data collection, data distribution and related technologies caused researchers to investigate current data mining algorithms from a new point of view. This is personal privacy.
Tolga Berberoglu, Mehmet Kaya
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Quantitative association rules over incomplete data

SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 2002
This paper explores the use of principle component analysis (PCA) to estimate missing values during the mining of quantitative association rules. An example of such association may be "15% of customers spend $100-$300 every month will have two cable outlets at home".
Vincent Ng 0002, John Lee
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Density-Based Mining of Quantitative Association Rules

2000
Many algorithms have been proposed for mining of boolean association rules. However, very little work has been done in mining quantitative association rules. Although we can transform quantitative attributes into boolean attributes, this approach is not effective and is difficult to scale up for high dimensional case and also may result in many ...
Yiu, SM, Wang, L, Cheung, DWL, Zhou, B
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Mining quantitative association rule of earthquake data

Proceedings of the 2009 International Conference on Hybrid Information Technology, 2009
Earthquake is a natural disaster which causes extensive poverty damage as well as the death of thousands and thousands of people. In this study, we tried to find the unknown characteristics of earthquakes using association rule mining methods global earthquake data occurred since 1973.
Jin A. Lee, Jong Gyu Han, Kwang Hoon Chi
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Mining Quantitative Association Rules on Overlapped Intervals

2005
Mining association rules is an important problem in data mining. Algorithms for mining boolean data have been well studied and documented, but they cannot deal with quantitative and categorical data directly. For quantitative attributes, the general idea is partitioning the domain of a quantitative attribute into intervals, and applying boolean ...
Qiang Tong, Baoping Yan, Yuanchun Zhou
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Quantitative Association Rules Based on Distance

2009 International Conference on Computational Intelligence and Software Engineering, 2009
In association analysis, mining the continuous attributes may reveal useful and interesting insights about the data objects which are of continuous attributes. Quantitative association rules are aimed to deal with the relationships among continuous attributes of data objects.
Hai-Dong Meng, Yu-Chen Song, Hai-Tao Shen   +2 more
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Mining High Utility Quantitative Association Rules

2007
Mining weighted association rules considers the profits of items in a transaction database, such that the association rules about important items can be discovered. However, high profit items may not always be high revenue products, since purchased quantities of items would also influence the revenue for the items. This paper considers both profits and
Show-Jane Yen, Yue-Shi Lee
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A quantitative and qualitative ANALYSIS of blocking in association rule hiding

Proceedings of the 2004 ACM workshop on Privacy in the electronic society, 2004
Data mining provides the opportunity to extract useful information from large databases. Various techniques have been proposed in this context in order to extract this information in the most efficient way. However, efficiency is not our only concern in this study.
Emmanuel D. Pontikakis, Yannis Theodoridis, Achilleas A. Tsitsonis, LiWu Chang, Vassilios S. Verykios   +4 more
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Optimized fuzzy association rule mining for quantitative data

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014
With the advance of computing and electronic technology, quantitative data, for example, continuous data (i.e., sequences of floating point numbers), become vital and have wide applications, such as for analysis of sensor data streams and financial data streams.
Hui Zheng 0001, Jing He 0004, Guangyan Huang, Yanchun Zhang   +3 more
openaire   +2 more sources

Using Quantitative Association Rules in Collaborative Filtering

2005
Recommender systems make information filtering for user by predicting user’s preference to items. Collaborative filtering is the most popular technique in implementing a recommender system. Association rule mining is a powerful data mining method to search for interesting relationships between items by finding the items frequently appeared together in ...
Xiaohua Sun, Fansheng Kong, Hong Chen
openaire   +1 more source