Results 221 to 230 of about 60,734 (264)

Mining quantitative association rules under inequality constraints

Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453), 2003
In the past several years, there has been much active work in developing algorithms for mining association rules. However, in many real-life situations, not all association rules are of interest to the user. A user may want to find association rules which satisfy a given inequality constraint for a set of quantitative items.
null Charles Lo, null Vincent Ng
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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, JongGyu Han, Kwang Hoon Chi
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Mining Quantitative and Fuzzy Association Rules

2005
The problem of mining association rules from databases was introduced by Agrawal, Imielinski, & Swami (1993). In this problem, we give a set of items and a large collection of transactions, which are subsets (baskets) of these items. The task is to find relationships between the occurrences of various items within those baskets.
Hong Shen, Susumu Horiguchi
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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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Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Applied Intelligence, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hong, Tzung-Pei, Lin, Kuei-Ying, Chien, Been-Chian   +2 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, Jing He, Guangyan Huang, Yanchun Zhang   +3 more
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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 Mining Methods with Privacy-preserving

Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005
Considering the different size of quantitative attribute values and categorical attribute values in databases, we present two quantitative association rules mining methods with privacy-preserving respectively, one bases on Boolean association rules, which is suitable for the smaller size of quantitative attribute values and categorical attribute values
null Zi-Yang Chen, null Guo-Hua Liu
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Effective Mining of Fuzzy Quantitative Weighted Association Rules

2010 International Conference on E-Business and E-Government, 2010
This paper presents a new method of mining weighted association rules, which can hold the “weighted downward closed property” by using an improved model of weighted support measurements in the weighted setting. Compared to some generalized weighted association rules mining, it proves that the method can quickly and efficiently mine important ...
Li Cheng-jun, Yang Tian-qi
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Mining quantitative association rules in large relational tables

ACM SIGMOD Record, 1996
We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by fine-partitioning the values of the attribute and then combining ...
Ramakrishnan Srikant, Rakesh Agrawal
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