PRICES: An Efficient Algorithm for Mining Association Rules

Wang, Chuan; Tjortjis, Christos

doi:10.1007/978-3-540-28651-6_52

Cited by 22 publications

(15 citation statements)

References 4 publications

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“…161-176, © 2013 INFORMS find all the frequent itemsets (i.e., sets of itemsets whose support is sufficiently high), and the second stage is to derive association rules from the frequent itemsets (confidence is often used as the measurement to select significant association rules). Some of the most popular association rule mining techniques include Apriori (Agrawal and Srikant 1994), Eclat (Zaki 2000), FP-growth , tree projection (Agarwal et al 2001), and PRICES (Wang and Tjortjis 2004) (see Kotsiantis and Kanellopoulos 2006 for more details on these algorithms). Our definition of OS-pattern is very different from that of an association rule, and the traditional association rule mining techniques cannot discover the type of online shopping patterns we are interested in.…”

Section: Related Workmentioning

confidence: 99%

Discovery of Online Shopping Patterns Across Websites

Yang

Liu

Cai

2013

INFORMS Journal on Computing

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I n the online world, customers can easily navigate to different online stores to make purchases. The products purchased on one site are often associated with product purchases on other sites (e.g., a hotel reservation on one site and a car rental on another site). Whereas market basket analysis is often used to discover associations among products for brick-and-mortar stores, it is rarely applied in the online setting where consumers navigate among different online stores to buy products. We define online shopping patterns and develop two novel methods to perform market basket analysis across websites. While this research is motivated by online shopping applications, our contribution is mainly methodological. The two methods we develop in this paper can not only be used to identify various online shopping patterns across sites and products but can also be applied to settings where patterns exist across different dimensions. Experiments on both synthetic data and real online shopping data demonstrate the effectiveness of our methods.

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Section: Related Workmentioning

confidence: 99%

Discovery of Online Shopping Patterns Across Websites

Yang

Liu

Cai

2013

INFORMS Journal on Computing

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“…PRICES: a skilled algorithm developed by Chuan Wang [29] in 2004, which first recognizes all large itemsets used to construct association rules. This algorithm decreased the time of large itemset generation by scanning the database just once and by logical operations in the process.…”

Section: Dsm-fi: Data Stream Mining For Frequent Itemsets Is a Novel mentioning

confidence: 99%

Efficient Analysis of Pattern and Association Rule Mining Approaches

Slimani¹,

Lazzez²

2014

IJITCS

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Abstract-The process of data mining produces various patterns from a given data source. The most recognized data mining tasks are the process of discovering frequent itemsets, frequent sequential patterns, frequent sequential rules and frequent association rules. Numerous efficient algorithms have been proposed to do the above processes. Frequent pattern mining has been a focused topic in data mining research with a good number of references in literature and for that reason an important progress has been made, varying from performant algorithms for frequent itemset mining in transaction databases to complex algorithms, such as sequential pattern mining, structured pattern mining, correlation mining. Association Rule mining (ARM) is one of the utmost current data mining techniques designed to group objects together from large databases aiming to extract the interesting correlation and relation among huge amount of data. In this article, we provide a brief review and analysis of the current status of frequent pattern mining and discuss some promising research directions. Additionally, this paper includes a comparative study between the performance of the described approaches.

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“…Chuan Wang and Christos Tjortjis [18] have proposed an efficient algorithm for mining association rules, which first identifies all large itemsets and then generates association rules. Their approach has reduced large itemset generation time, known to be the most time-consuming step, by scanning the database only once and using logical operations in the process.…”

Section: Related Workmentioning

confidence: 99%

“…Even though several algorithms are available in the literature for association rule mining, [12][13][14][15][16][17][18][19][20] a good number of them deal with efficient implementations rather than the production of effective rules [11,16,18]. The techniques that aid in the extraction of suitable and genuine association patterns are mostly quantitative in nature [10,12,13,17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Utility Sentient Approach for Mining Interesting Association Rules

Shankar¹

2009

IJET

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Utility-based data mining is a new research area that concentrates on all types of utility factors in data mining processes and is targeted at incorporating utility considerations in both predictive and descriptive data mining tasks. Discovering interesting association rules that are utilized to improve the business utility of an enterprise has long been recognized in data mining community. This necessitates identifying interesting association patterns that are both statistically and semantically important to the business utility. Classical association rule mining techniques are capable of identifying interesting association patterns but they have failed to associate the user's objective and utility in mining. In this paper, we have proposed an approach for mining novel interesting association patterns from transaction data items of an enterprise to improve its business utility. The approach mines novel interesting association patterns by providing importance to significance, utility and subjective interestingness of the users. The novel interesting patterns mined using proposed approach can be used to provide valuable suggestions to the enterprise to improve its business.

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PRICES: An Efficient Algorithm for Mining Association Rules

Cited by 22 publications

References 4 publications

Discovery of Online Shopping Patterns Across Websites

Discovery of Online Shopping Patterns Across Websites

Efficient Analysis of Pattern and Association Rule Mining Approaches

A Novel Utility Sentient Approach for Mining Interesting Association Rules

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