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Proceedings of the tenth international conference on Information and knowledge management, 2001
Association rule mining is a well-researched area where many algorithms have been proposed to improve the speed of mining. In this paper, we propose an innovative algorithm called Rapid Association Rule Mining (RARM) to once again break this speed barrier. It uses a versatile tree structure known as the Support-Ordered Trie Itemset (SOTrieIT) structure
Amitabha Das +2 more
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Association rule mining is a well-researched area where many algorithms have been proposed to improve the speed of mining. In this paper, we propose an innovative algorithm called Rapid Association Rule Mining (RARM) to once again break this speed barrier. It uses a versatile tree structure known as the Support-Ordered Trie Itemset (SOTrieIT) structure
Amitabha Das +2 more
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Temporal Association Rule Mining
2015A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks of Dow Jones Industrial Average DJIA in terms of highest daily volume and DJIA index time series, expressed in points are used to form the time-series database TSDB from 1994 to 2007.
Ting-Feng Tan +5 more
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Distributed Association Rule Mining
2005Data mining is an iterative and interactive process that explores and analyzes voluminous digital data to discover valid, novel, and meaningful patterns (Mohammed, 1999). Since digital data may have terabytes of records, data mining techniques aim to find patterns using computationally efficient techniques.
Mafruz Zaman Ashrafi +2 more
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Mining Negative Association Rules
IOSR Journal of Computer Engineering, 2012Association rule mining is a data mining task that discovers associations among items in a transactional database. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e ...
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Subjective Association Rule Mining
Proceedings of the 2018 10th International Conference on Machine Learning and Computing, 2018Human behavior is complex and ambiguous. This study investigates how human beings consider two significant elements of discrete items: one side is their preference ranking relationships, while the other is their preferred temporal relationships. The development of a means of deriving valuable information from collected user opinions of these two ...
Pu-Tai Yang +3 more
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2010
The notion of finding rare association rules is like finding precious gems in an open field; it is a daunting task but, if successful, it is very rewarding. Association rule mining systems, such as Apriori, generally employ an exhaustive search algorithm.
Yun Sing Koh, Nathan Rountree
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The notion of finding rare association rules is like finding precious gems in an open field; it is a daunting task but, if successful, it is very rewarding. Association rule mining systems, such as Apriori, generally employ an exhaustive search algorithm.
Yun Sing Koh, Nathan Rountree
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Mining Spatial Association Rules
2004This paper focuses on finding spatial association rules. In the first part of the article the specifics of spatial databases are discussed and the existing methods for finding spatial association rules are reviewed. Also different classes of spatial relations used to express the relationships between objects in spatial databases are presented.
Robert Bembenik, Grzegorz Protaziuk
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Combined Association Rule Mining
2008This paper proposes an algorithm to discover novel association rules, combined association rules. Compared with conventional association rule, this combined association rule allows users to perform actions directly. Combined association rules are always organized as rule sets, each of which is composed of a number of single combined association rules ...
Huaifeng Zhang +3 more
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Mining Vague Association Rules
2007In many online shopping applications, traditional Association Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold. For example, those items that are put into the basket but not checked out.
An Lu +3 more
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